Doctoral scholarships

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both Home and Overseas students. Please note that only Home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Professor Ling Qian (L.Qian@mmu.ac.uk)

Project advert

The study of wave/wind structure interaction (WSI) is fundamental to the design and development of coastal, offshore and ocean engineering structures, including sea defences and emerging offshore renewable energy (ORE) technologies (for conversion of wind, wave and tidal energy to electricity). With the wide availability and constant improvement of in-house, commercial and open-source codes, covering the full spectrum of model fidelity and efficiency, numerical modelling in the form of a so-called numerical wave tank (NWT) has become an indispensable part of WSI research and industrial applications.  However, modelling applications involving complex WSI accurately and efficiently, especially those associated with extreme marine conditions, is particularly challenging because these are multi-physics, multi-scale problems. As a consequence, coupling of multiple codes, with different capabilities, may become the only practical solution. However, within the current WSI code coupling framework, coupling between flow solvers is done on an ad hoc basis through a predefined fixed interface, limiting its applications to simple WSI problems and the potential gains in computational efficiency from code coupling.

Aims and objectives

The aim of the project is to develop an advanced coupled framework for Numerical Wave Tank (NWT) software with enhanced functionality, accuracy and efficiency for modelling complex wave structure interaction problems involving moving structures and multiple flow physics. The aim will be achieved through the following objectives:

• Implement a dynamic code coupling interface for coupling hierarchical flow and structural solvers within an NWT.
• Optimise multi-model coupling, including designing and implementing conservative interpolation schemes as well as its implementation on high-performance computers.
• Apply the developed code to evaluate the performance and survivability of emerging offshore renewable technologies such as floating wind and wave energy converters.
• Prepare papers for publication in high-quality scientific journals.

Specific requirements of the candidate

Potential candidates should have or expect to obtain a first or upper-second Honours degree, or equivalent, in Engineering, Mathematics or Physical Sciences. Knowledge of numerical solutions of partial differential equations and/or fluid dynamics and good programming skills are essential. A masters degree in a relevant subject would be an advantage. Experience of applying OpenFOAM and high-performance computing (HPC) in the context of CFD would be beneficial.

How to apply

Interested applicants should contact Prof Ling Qian at L.Qian@mmu.ac.uk  for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Computing and Digital Technology (or download the PGR application form).

You should also submit a personal statement and a CV (traditional CV or narrative CV) addressing the project’s aims and objectives, demonstrating how the skills you have map to this area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 30 June 2024.

Expected start date: October 2024.

Please quote the reference: SciEng-LQ-2024-CFD

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both Home and Overseas students. Please note that only Home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Dr Connah Kendrick (connah.kendrick@mmu.ac.uk)

Project advert

The management of chronic wounds poses a considerable burden on healthcare systems, with approximately 2.2 million patients currently afflicted, resulting in annual costs of £5.3 billion for the NHS to address wound care and its associated comorbidities, including amputations.

The logistical challenges of transporting vulnerable patients to and from hospitals incur additional costs and elevate the risk of infections, leading to a significant rise in patient mortalities. Addressing this complex scenario necessitates an accurate and automated computerised solution for measuring and characterising wound areas, which is currently non-existent.

This research project aims to develop an innovative digital technology solution to enhance clinicians’ confidence in monitoring wounds and facilitating remote assessment and monitoring. By enabling at-home tracking, the system aims to encourage more regular checks and prompt responses to declines in recovery, ultimately fostering early intervention. We partner with industrial partners and clinicians to help provide a real-world solution to facilitate the effective treatment of DFU.

This approach contributes to reducing healthcare costs and engenders greater trust in digital technology among end-users, thereby enhancing overall patient care- the collaborative development of this system with renowned researchers in the field of AI for wound monitoring and a chance to work with clinical and industrial partners.

Aims and objectives

The proposed research project aims to design innovative multimodal intelligent techniques to measure diabetic foot ulcers and wounds accurately. The research objectives are to:

1. Create a world-leading multi-modal digital wound repository.
2. Design an innovative 2.5D modelling tool for wound assessment.
3. Create a novel algorithm using a multimodal dataset to improve the accuracy of predicting ulcer/wound healing.

Specific requirements of the project

The successful candidate would have a strong background in Computer Science, Engineering, Maths or Physics, and preference would be given to those with a good understanding of computer vision and deep learning.

It is essential for them to have a good background knowledge of machine learning and computer programming and a proactive approach to their work.

A self-motivated, driven, and creative individual will push the bounds of existing research by our world-leading team: Yap, M.H., Kendrick, C. and Cassidy, B. eds., 2023. Diabetic Foot Ulcers Grand Challenge: Third Challenge, DFUC 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings (Vol. 13797). Springer Nature.

How to apply

Interested applicants should contact Dr Connah Kendrick (connah.kendrick@mmu.ac.uk) for an informal discussion. To apply, you must:

• Complete the online application form for a full-time PhD in Computing and Digital Technologies (or download the PGR application form).
• Complete the PGR thesis proposal form addressing the project’s aims and objectives, demonstrating how your skills relate to the area of research and why you see this area as important and interesting.
• Applicants should ensure their submitted CV clearly demonstrates any experience and work in ML and AI

If applying online, you must upload your statement in the supporting documents section or email the application form and statement to PGRAdmissions@mmu.ac.uk. The closing date is 1 July 2024. The expected start date is October 2024.

Please quote the reference: SciEng-CK-2024-diabetic-foot-ulcer.

This is a full-time, funded PhD opportunity in the Faculty of Business and Law. It is open to both Home and Overseas students. Please note that only Home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Professor Timothy Jung (t.jung@mmu.ac.uk) and Dr Mandy Claudia tom Dieck (c.tom-dieck@mmu.ac.uk)

Project advert

The latest advancements in extended reality (XR) and artificial intelligence (AI) are creating fresh opportunities for both businesses and consumers.

Anticipated innovations in the metaverse promise exciting new use cases. However, there is a limited understanding of suitable business models and the integration of blockchains and NFTs.

This study is set within the context of the rapidly growing global eSports and entertainment industry. Given the substantial potential to disrupt current processes and content consumption methods through the metaverse, exploring new business models is crucial.

This PhD research aims to fill the gap in existing literature by employing mixed methods to understand various stakeholders and by proposing and validating a novel metaverse business model for the eSports and entertainment sectors.

This project supports the wider research vision of the faculty and university which aims to transform lives, business and communities through world-class research and impactful international collaborations with industrial stakeholders.

Aims and objectives

The project aims to explore, propose and validate a new metaverse business model within the eSports and entertainment context.

This PhD project aims to address the following research objectives:

1. To explore the concept of metaverse and existing digital business models within the entertainment and eSport context.

2. To investigate the use of blockchain technology and non-fungible tokens (NFTs).

3. To construct and validate a new metaverse business model for the eSports and entertainment context.

Specific requirements of the project

The successful candidate should have a passion for the subject area of metaverse and its impact on societies and particularly eSports and entertainment.  

The successful candidate will be expected to actively engage in the department’s research community through dissemination of findings and engagement in research activities.

A previous degree or research experience in this subject related area would be beneficial.

The successful candidate should have a good understanding of research methodologies and be willing to engage with new methods.

How to apply

Interested applicants should contact Professor Timothy Jung (t.jung@mmu.ac.uk) or Dr Mandy Claudia tom Dieck (c.tom-dieck@mmu.ac.uk) for an informal discussion.

To apply, you must:

• Complete the online application form for a full-time PhD in Operations and Technology (or download the PGR application form).
• Complete the PGR thesis proposal form addressing the project’s aims and objectives, demonstrating how your skills relate to the area of research and why you see this area as important and interesting.
• Applicants should ensure their submitted CV clearly demonstrates any experience and work in ML and AI

If applying online, you must upload your statement in the supporting documents section or email the application form and statement to PGRAdmissions@mmu.ac.uk. The closing date is 4 July 2024. The expected start date is October 2024.

Please quote the reference: BusLaw-TJ-2024-Metaverse-Business-Model

This is a full-time, funded PhD opportunity in the Faculty of Business and Law. It is open to both Home and Overseas students. Please note that only Home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Professor Timothy Jung (t.jung@mmu.ac.uk) and Dr Mandy Claudia tom Dieck (c.tom-dieck@mmu.ac.uk)

Project advert

The recent pandemic has accelerated the adoption of extended realities (XR) and the metaverse.

However, there is limited research on the socio-legal implications and challenges posed by these technologies.

This study focuses on the eSports and entertainment industry, a rapidly growing global sector increasingly integrating XR. Given XR’s significant potential to disrupt existing processes and lifestyles, it is crucial to explore ethical considerations, security, and privacy issues to ensure successful implementation in both industry and society.

This PhD research aims to fill the gap in the literature by using mixed methods to understand various stakeholders and by proposing and validating a socio-legal framework for XR and the metaverse within the eSports and entertainment industry.

This project supports the wider research vision of the faculty and university which aims to transform lives, business and communities through world-class research and impactful international collaborations with industrial stakeholders.

Aims and objectives

The project aims to explore socio-legal implications and challenges of extended realities (XR) within the eSports and entertainment context.

This PhD project aims to address the following research objectives:

1. To investigate the socio-legal implications of extended realties (XR).

2. To identify ethical, security, privacy and legal challenges of XR within the eSports and entertainment context

3. To construct a theoretical framework for understanding the socio-legal implications of XR and disruption of the traditional eSports and entertainment industry.

Specific requirements of the project

The successful candidate should have a passion for the subject area of extended realities and their impact on societies and particularly eSports and entertainment.

The successful candidate is expected to engage in the department’s research community through active dissemination. A previous degree or research experience in this subject related area would be beneficial.

The successful candidate should have a good understanding of research methodologies and be willing to engage with new methods.

How to apply

Interested applicants should contact Professor Timothy Jung (t.jung@mmu.ac.uk) or Dr Mandy Claudia tom Dieck (c.tom-dieck@mmu.ac.uk) for an informal discussion.

To apply, you must:

• Complete the online application form for a full-time PhD in Operations and Technology (or download the PGR application form).
• Complete the PGR thesis proposal form addressing the project’s aims and objectives, demonstrating how your skills relate to the area of research and why you see this area as important and interesting.
• Applicants should ensure their submitted CV clearly demonstrates any experience and work in ML and AI

If applying online, you must upload your statement in the supporting documents section or email the application form and statement to PGRAdmissions@mmu.ac.uk. The closing date is 4 July 2024. The expected start date is October 2024.

Please quote the reference: BusLaw-TJ-2024-extended-realities

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to Home students only. 

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Dr Simon Christie (s.christie@mmu.ac.uk) and Dr Christopher Paling (c.paling@mmu.ac.uk)

Project advert

Hydrogen fuel is expected to play an important role in the decarbonisation of global aviation, with the potential to reduce greenhouse gas emissions from many airport activities and flights.

However, there are significant challenges, including requirements for new business models and collaborations, differently skilled workers, revised safety cases and procedures, new fuel and power supplies for airports, and new airport infrastructure.

Some research exists and there are several pilot demonstrations of hydrogen fuel use at airports around the world. However, knowledge and tools for assessing the potential and benefits of hydrogen fuel use in various scenarios at airports is under-developed.

This project will provide new insights and increase our understanding of these challenges, in particular in relation to new fuel supplies and new airport infrastructure, developing methods to assess the various opportunities, and how best to overcome barriers to the changes needed to shift to hydrogen-based solutions.

This studentship is a collaboration with Manchester Airports Group (MAG), and will use Manchester Airport, UK as a case study to apply and further develop the research.

Aims and objectives

The objectives of the studentship are to:

1. Understand the opportunities (scenarios) and barriers for hydrogen fuel use at airports in the context of reducing an airport operator’s scope 1, scope 2 and scope 3 greenhouse gas emissions.
2. Develop criteria/methods/tools to assess each scenario - establish appropriate impact criteria for assessment (e.g. practicality, agency of airport operator, greenhouse gas emissions, cost, sustainability, viability, and safety).
3. Evaluate scenarios in line with methods/criteria/tools produced above.
4. Determine the requirements for new supply chains, collaborations, airport infrastructure and operational procedures.
5. Map scenarios onto a pathway and timeline towards transitioning to net zero greenhouse gas emissions.

Manchester Airport, UK will be used as a case study to validate research outcomes (with relevance to objectives 4 and 5).

Specific requirements of the project

Essential

• First or upper second class BSc honours degree in a relevant subject such as environmental science or environmental management.
• Experience of collecting or handling qualitative and quantitative data.
• Proficient in Microsoft Office, including Microsoft Excel.

Desirable

• MSc in a relevant subject.
• Evidence of engagement in research activities such as managing a research project, publishing journal articles, conference presentations.
• Evidence of an interest in the subject of the studentship.

How to apply

To apply you will need to complete the online application form for a full-time PhD in Environmental Sciences (or download the PGR application form).

You should also complete the (PGR thesis proposal /Narrative CV) form addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Interested applicants should contact Dr Christopher Paling (c.paling@mmu.ac.uk) for an informal discussion.

If applying online, you must upload your statement in the supporting documents section or email the application form and statement to PGRAdmissions@mmu.ac.uk. The closing date is 4 July 2024. The expected start date is October 2024.

Please quote the reference: SciEng-SC-2024-Hydrogen-Fuel

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both Home and Overseas students. Please note that only Home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Professor Peter Kelly (peter.kelly@mmu.ac.uk)

Project advert

This project provides an exciting opportunity to undertake cutting-edge research and development into new products to support the low-carbon energy sector.

We will focus on the production and testing of new coatings for cladding material (i.e. fuel rods) used in nuclear energy generation, that will be designed to provide increased oxidation resistance coupled with resistance to mechanical damage. The development of these coatings will contribute to enhancing the safety and efficiency of nuclear power plants, which are a key component of the UK’s low carbon energy strategy on the route to net zero.

You will be a member of the Surface Engineering Group and the research will be carried out in the Surface Engineering Laboratory at Manchester Metropolitan University (including the brand new Dalton Tower facilities), which is equipped with state-of-the-art deposition and testing facilities and also at our industrial partner’s site. The project will include industrial supervision as well as travel to attend conferences and meetings both within the UK and globally.

The appointee will be working as part of an experienced team of academic and industrial researchers and will be provided with all necessary training and supervision to achieve the aims of the project.

Aims and objectives

This project aims to improve the performance of fuel rod claddings through the introduction of a protective surface coating, which will be deposited using magnetron sputtering techniques. This is a widely used technique with many industrial applications.

To achieve this, we will develop novel chromium alloy coatings with improved chemical and mechanical properties, and to explore other innovative coating strategies to optimise the performance of the coatings.

Specific requirements of the project

The candidate will have a background in engineering or materials science and a keen interest in research. Experience of thin film deposition (PVD) and characterisation (e.g. SEM, EDX, XRD, wear testing) techniques is desirable, but is not essential as all training will be provided.

The candidate will need to demonstrate adaptability due to the multi-disciplinary nature of the work, and the capacity to carry out experimental work safely, and with precision.

An ability to work as part of a diverse team, meet deadlines and produce reports and presentations of a high standard to a range of audiences is essential.

Applicants will require initiative, self-motivation, good communication skills, and the ability to critically evaluate their work.

A willingness and ability to travel is an advantage, as the project may involve a short period of work at collaborating groups.

How to apply

Interested applicants should contact Professor Peter Kelly for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Engineering (or download the PGR application form).

You should also complete a CV (standard or Narrative CV) demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 8 July 2024.

Expected start date: October 2024.

Please quote the reference: SciEng-PK-2024-Nuclear-Fuel-Rods

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both Home and Overseas students. Please note that only Home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Dr Greg Wood (greg.wood@mmu.ac.uk )

Project advert

Video assistant referee (VAR) decisions occur at critical game incidents and require accurate and timely decisions under intense scrutiny. However, little is known about expertise in this context. Additionally, to enhance decision accuracy, match officials should undergo role-specific decision-making training, yet current training methods are limited particularly in the context of VAR.

This project forms part of an exciting new research and innovation partnership between Manchester Metropolitan University and the Professional Game Match Officials Limited (PGMOL).

This fully funded PhD provides an exciting opportunity to pursue postgraduate study within Manchester Metropolitan University’s Institute of Sport, which is equipped with state-of-the-art sport science testing facilities, while also gaining first-hand industry experience and mentorship from professional match officials and support staff at the PGMOL.

Aims and objectives

The aims of this fully funded PhD are to first quantify expertise in VAR decision-making and highlight constraints that undermine this process (such as time, technology, and psychological stress).

We then aim to test training interventions designed to enhance this decision-making process under the intense pressure of real match scenarios. 

Specific requirements of the project

Essential

• A first or upper second-class BSc honours degree in sport and exercise science, sport psychology, psychology, or another related discipline
• An ability to critique and analyse scientific evidence, methodology and data
• An ability to interpret and communicate complex data in terms that are easily understood by a variety of audiences
• An ability to maintain records and the organisation of data files using Microsoft Excel
• Excellent written and oral communication skills

Desirable

• An MSc in sport and exercise science, sport psychology, psychology, or another related discipline
• Experience of refereeing in football or sport
• Other accreditations commensurate to the role (such as BASES accreditation)
• Industry experience within the sport and exercise sciences working in an applied manner
• A proficiency in using statistical and/or analytical software packages such as SPSS, R, Tableau, and Power BI

How to apply

Interested applicants should contact Dr Greg Wood (greg.wood@mmu.ac.uk)  for an informal discussion. Additional project fees are £2,000.

To apply you will need to complete the online application form for a full-time PhD in Sport and Exercise Science (or download the PGR application form).

You should also complete the PGR thesis proposal (supplementary information) form and CV addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 15 July 2024.

Expected start date: October 2024.

Please quote the reference: SciEng-GW-2024-SA-PGMOL

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both home and overseas students. Please note that only Home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Professor Warren Gregson (w.gregson@mmu.ac.uk)

Project advert

As talent recruitment in high-performance sport moves towards more data-driven forecasting models there is a need to understand how we combine both objective and subjective information to make more accurate predictions and determine what information is required to optimise these judgements.

In collaboration with Manchester United FC and MMU, a 3-year PhD studentship has arisen within the Manchester United FC scouting department to conduct research related to talent recruitment.

The overarching aim of the PhD is to design an accurate and resilient operational model to better understand how talent scouts at Manchester Utd FC document, validate and monitor their selection judgements and how these judgements complement the data analytics provided by the data science department. 

This PhD will be addressing a complex problem and therefore will need to be solution-focused and translational in nature. It will need to draw on various disciplines with the goal of integrating insights to develop our understanding.

Aims and objectives

The aim is to review the current Manchester United FC recruitment framework(s), validate the subjective judgements of talent scouts and implement an internal scout development framework.

This project will aim to answer the following research questions:

• What is the current research landscape regarding documenting, validating and monitoring scouting judgements in professional football?
• What is the language, terminology and textual architecture adopted by Manchester Utd talent scouts?
• What level of consensus (which is to say validity and reliability) exists between the Manchester Utd FC player selection attributes and at-distance talent scouts?
• What do talent scouts look for and think when undertaking a scouting exercise?
• How do talent scouts document, validate and monitor their selection judgements?

Specific requirements of the project

Essential

• A first class or upper second-class (2:1) degree (or equivalent) in a sport-related discipline.
• One year minimum experience in providing/supporting talent recruitment or scouting expertise in performance sport
• An ability to critique and analyse scientific evidence, methodology and data
• Strong interpersonal, communication and organisational skills
• Experience of using online scouting/recruitment platforms (such as WyScout/Hudl)
• Proficient with Microsoft Office

Desirable

• MSc or research masters in a sport-related discipline.
• Sports Science/Sports Medicine/Performance Analysis accreditation (i.e., BASES etc.)
• Completion of Talent Identification and Scouting in Football Award (Level 2).
• Proficient at conducting database searching and evidence of synthesis. 

• Competent with associated video technology

How to apply

Interested applicants should contact Professor Warren Gregson for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Sports Science (Performance) (or download the PGR application form).

You should also complete the (Narrative CV) form addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 15 July 2024.

Expected start date: October 2024.

Please quote the reference: SciEng-WG-2024-talent-scouts

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both home and overseas students. Please note that only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Dr Xinqi Fan (x.fan@mmu.ac.uk) 

Project advert

The project forms part of an exciting new research and innovation partnership between Manchester Metropolitan University and the Professional Game Match Officials Limited.

This project aims to develop deep insights into the spatiotemporal characteristics of soccer match officials’ movement and positioning in the context of decision-making. The successful candidate will be embedded across the two institutions, thereby undertaking empirical research while gaining first-hand industry experience and mentorship from professionals across the fields of sports science and data science.

The PhD will apply cutting-edge AI techniques to the data captured in soccer game situations and a multi-modal approach is proposed to extract actionable insights from the plethora of match data available. Analysis has become an integral part of match official coaching, and this PhD seeks to develop and extend the current practice of match official coaching, evaluation, and preparation.

A background in AI is essential, with links to sports science desirable. However, training will be provided to the successful candidate by the highly experienced supervisory team.

Aims and objectives

This PhD studentship has the following objectives:

• To explore and profile the spatiotemporal patterns of soccer match officials’ in the context of decision-making.
• Investigate which movement variables and characteristics are sensitive to soccer match officials’ decision-making.
• Develop statistical models to explain and predict decision-making in soccer matches.

Specific requirements of the project

Essential Criteria:

• A first-class or upper second-class (2:1) degree (or equivalent) in a relevant discipline such as mathematics, computer science, AI, data science or statistics.
• Experience in training machine models with at least one of the following (Python, MatLab, R))
• An ability to critique and analyse scientific evidence, methodology and data.
• Strong interpersonal, communication and organisational skills.

Desirable Criteria:

• M.Sc. or Research Masters in a relevant discipline such as mathematics, computer science, data science or statistics.
• Experience with event and/or tracking data in sport.

How to apply

Interested applicants should contact Dr Xinqi Fan for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Computing and Digital Technologies (or download the PGR application form).

You should also complete the (PGR thesis proposal) form addressing the project’s aims and objectives, and a CV, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 15 July 2024.

Expected start date: October 2024.

Please quote the reference:SciEng-XF-2024-spatiotemporal-investigation

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both home and overseas students. Please note that only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Dr Aneurin J Kennerley (a.kennerley@mmu.ac.uk)

Project advert

The project forms part of an exciting new research and innovation partnership between Manchester Metropolitan University and Manchester United Football Club.

The successful candidate will be embedded across the two institutions, thereby undertaking developmental research on two in-house 3 Telsa magnetic resonance imaging (MRI) scanners. This project aims to develop novel MRI methodologies and analysis software to drive improved health and performance outcomes for players.

You will develop innovative real-time imaging for achieving unparalleled 3D visualization of lower limb joint movements to inform wider exploration of muscle/bone analytics for automatic injury identification.

The successful candidate will have programming experience, in particular training AI models, preferably in an applied imaging/sport setting. In post, you will gain comprehensive training on the operation of clinical MRI scanners. The successful candidate will be responsible for conducting all aspects of the research project, from data collection and analysis to disseminating the findings at scientific conferences and through publication in world-leading journals.

Aims and objectives

The project aims to deliver AI-empowered innovative dynamic imaging information and key kinetic metric outputs concerning football player mobility (in particular lower limb joint/muscle/bone) for use by Manchester United Football Club. This state-of-the-art approach to real-time imaging will be integrated into an AI imaging analytics package to promote automatic injury detection and aid player diagnosis by the partner medical team.

You will meet this aim by completing the following key objectives:

• Development/programming of real-time dynamic imaging of joint motion (>70 frames per second - based on multi-band radial k-space sampling and sparse reconstruction) in the Canon Sequence Development Environment.
• Implementation of AI models to empower hybrid imaging – merging the benefits of low spatial resolution real-time dynamic information with high-resolution structural imaging.
• Integrating dynamic/hybrid imaging into wider software-based AI imaging analytics of muscle asymmetry, fat infiltration, oedema and scaring.
• Use deep learning to deliver automatic injury detection methodologies to reduce false negative assessments.
• Explore the application of the developed methodologies after injury onset to track recovery metrics following training strategies implemented by the partner medical team.

Your research and development in this imaging space will have an immediate impact on a world-leading football club. In addition, the techniques you devise will find wider application across musculoskeletal research frameworks.

Specific requirements of the project

Essential criteria

• A first-class or upper second-class (2:1) degree (or equivalent) in a relevant discipline such as physics, mathematics, computer science, AI, data science or statistics. Strong candidates with sports science, physiotherapy, radiography or sports medicine-related degrees will also be considered.
• Experience in programming, in particular training machine models with at least one of the following (Python, MATLAB, R).
• An ability to critique and analyse scientific evidence, methodology and data.
• Strong interpersonal, communication and organisational skills.
• Proficient with Microsoft Office, specifically Microsoft Excel.

Desirable criteria

• MSc or research masters in a relevant discipline such as physics, mathematics, computer science, data science or statistics.
• Experience with C++ and XML programming languages.
• Image data analysis or relevant signal processing skills.
• MRI imaging experience at 3 Tesla field strengths.

How to apply

Interested applicants should contact Dr Aneurin James Kennerley (a.kennerley@mmu.ac.uk) for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Computer Science (or download the PGR application form).

You should also complete a standard CV demonstrating how the skills you have, map to the area of research and a cover letter showing why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 15 July 2024

Expected start date: 7 October 2024

Please quote the reference: SciEng-AJK-2024-joint-muscle-mobility

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both home and overseas students. Please note that only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Dr Adam Field (A.field@mmu.ac.uk)

Project advert

Elite soccer officiating is a profession with substantial physical demand. Fitness testing is therefore incorporated into the match selection criteria by the Professional Game Match Officials Limited (PGMOL), which is the governing body responsible for the training, development, and mentoring of elite soccer match officials in England.

Here, only match officials attaining the minimum standard are eligible for appointments. Fitness tests therefore represent physical employment tests, so protocols need to reflect the critical physical tasks of elite soccer officiating.

While the physical demands of soccer officiating are influenced by many factors, match activities represent the critical and generic tasks of officiating. Once reliable and valid physical employment tests have been developed following a critical job task analysis, robust and defensible minimum standards should be determined.

This fully funded PhD provides an exciting opportunity to pursue postgraduate study within Manchester Metropolitan University’s Institute of Sport, which is equipped with state-of-the-art sport science testing facilities. The preferred candidate will also work closely with the PGMOL to develop and implement valid physical employment tests with robust minimum standards for the selection of elite soccer match officials.

Aims and objectives

The primary aim of this project is to develop physical employment test(s) for soccer match officials. The project will lead to the integration of the physical employment test(s) within the PGMOL as a practical tool to ensure robust minimum standards for the selection of elite soccer match officials.

The project will do so through:

• The completion of a job task analysis of elite soccer officiating to determine the frequency, importance, and physical demand of the tasks involved
• The design and development of physical employment test(s), including evaluations of reliability and validity
• The development of performance standards and cut scores to support with selection and progression
• The promotion and implementation of the physical employment tests within the PGMOL

Specific requirements of the project

Essential

• A first or upper second-class BSc honours degree in sport and exercise science or another related discipline
• An ability to critique and analyse scientific evidence, methodology and data
• An ability to interpret and communicate complex data in terms that are easily understood by a variety of audiences
• An ability to maintain records and the organisation of data files using Microsoft Excel
• Excellent written and oral communication skills

Desirable

• An MSc in sport and exercise science or another related discipline
• Other accreditations commensurate to the role (such as BASES accreditation)
• Industry experience within the sport and exercise sciences working in an applied manner
• Proficiency in using statistical and/or analytical software packages (such as SPSS, R, Tableau or Power BI)

How to apply

Interested applicants should contact Dr Adam Field for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Sport and Exercise Science (or download the PGR application form).

You should also complete the PGR thesis proposal and Narrative CVaddressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 15 July 2024.

Expected start date: October 2024.

Please quote the reference: SciEng-AF-2024-PGMOL-physical-employment-tests

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both home and overseas students. Please note that only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Dr Matthew Andrew (matthew.andrew@mmu.ac.uk)

Project advert

Talent identification is multifaceted and scientific research that provides evidence-informed information to key stakeholders (coaches, practitioners, scouts) to support their decisions regarding player identification, development, selection, and retention is emerging. A key area influencing talent identification is growth and maturation.

In collaboration with the Football Association of Wales and Manchester Metropolitan University, an opportunity for a 3-year PhD studentship embedded within the youth male national team of Wales is available. The PhD studentship will focus on collecting data relating to growth, maturation, and talent identification to support the youth national coaching and sport science staff.

The successful candidate will develop audits of the current scientific information on growth, maturation, and talent identification that are used to inform key stakeholder decisions. The candidate will also develop and validate new tests that aim to provide information on the multi-disciplinary performance skills of elite youth soccer players. The candidate will be responsible for all aspects of the research project, from data collection to publication.

Aims and objectives

The aim is to better understand growth, maturation, and talent identification processes within youth malesoccer in an international development programme.

The objectives of the project are:

• Evaluate the current scientific evidence around growth and maturation and talent identification in youth male soccer players
• Investigate how multi-disciplinary performance changes in relation to age and maturity status in youth male soccer players

Specific requirements of the project

Essential

• A first-class or upper second-class (2:1) degree (or equivalent) in Sports Science, Physiology, Medicine, Psychology, Performance Analysis, Coaching, or other Sports related degree.
• An ability to critique and analyse scientific evidence, methodology and data.
• Strong interpersonal, communication and organisational skills.
• Proficient with Microsoft Office, specifically Excel.

Desirable

• Experience working in soccer (roles such as sport scientist, physiologist, strength and conditioning coach).
• MSc or research masters in sports science, psychology, strength and conditioning or other sports-related degree.
• Sports science, strength and condition or sports medicine accreditation (such as BASES, ASCC, or CSCS).
• Proficient at conducting sport science testing including fitness, psychological and profiling assessments.
• Competent with programming/coding software (such as R or Python).

How to apply

Interested applicants should contact Dr Matthew Andrew (matthew.andrew@mmu.ac.uk) for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Sport and Exercise Science (or download the PGR application form).

You should also complete a CV (candidate’s choice of standard or Narrative CV) with a cover letter, addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 15 July 2024

Expected start date: October 2024

Please quote the reference: SciEng-MA-2024-FAW-Boys

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both home and overseas students. Please note that only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Dr Naomi Datson (N.Datson@mmu.ac.uk)

Project advert

Talent identification is multifaceted and scientific research that provides evidence-informed information to key stakeholders (coaches, practitioners, scouts) to support their decisions regarding player identification, development, selection, and retention is emerging. A key area influencing talent identification is growth and maturation.

In collaboration with the Football Association of Wales and Manchester Metropolitan University, an opportunity for a three-year PhD studentship embedded within the youth female national team of Wales is available. The PhD studentship will focus on collecting data relating to growth, maturation, and talent identification to support the youth national coaching and sport science staff.

The successful candidate will develop audits of the current scientific information on growth, maturation, and talent identification that are used to inform key stakeholder decisions. The candidate will also develop and validate new tests that aim to provide information on the multi-disciplinary performance skills of elite youth soccer players. The candidate will be responsible for all aspects of the research project, from data collection to publication.

Aims and objectives

The aim is to better understand growth, maturation, and talent identification processes within youth female soccer in an international development programme.

The objectives of the project are:

• Evaluate the current scientific evidence around growth and maturation and talent identification in youth female soccer players
• Investigate how multi-disciplinary performance changes in relation to age and maturity status in youth female soccer players

Specific requirements of the project

Essential

• A first-class or upper second-class (2:1) degree (or equivalent) in sports science, physiology, medicine, psychology, performance analysis, coaching, or other sports-related degree.
• An ability to critique and analyse scientific evidence, methodology and data.
• Strong interpersonal, communication and organisational skills.
• Proficient with Microsoft Office, specifically Excel.

Desirable

• Experience working in soccer (such as sport scientist, physiologist, strength and conditioning coach).
• MSc or research masters in sports science, psychology, strength and conditioning, or other sports-related degree.
• Sports science, sports and conditioning or sports medicine accreditation (such as BASES, ASCC or CSCS).
• Proficient at conducting sport science testing, including fitness, psychological and profiling assessments.
• Competent with programming/coding software (such as R or Python).

How to apply

Interested applicants should contact Dr Naomi Datson (N.Datson@mmu.ac.uk) for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Sport and Exercise Science (or download the PGR application form).

You should also complete a CV (candidate’s choice of standard or Narrative CV) with a cover letter, addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 15 July 2024

Expected start date: October 2024

Please quote the reference: SciEng-ND-2024-FAW-Girls

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is only open to home students. Home fees will be covered.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Professor Chris Murgatroyd (c.murgatroyd@mmu.ac.uk)

Project advert

This is a collaborative project between Manchester Metropolitan University and The University of Manchester Spin-Out company, Ravan Bio Ltd. Ravan Bio’s research has discovered new uses for existing approved drugs to inhibit the ageing process in mammals. The current project is to use a combination of molecular techniques, in vitro culture systems and studies in rodents to build on Ravan Bio’s findings, with the aim of refining dosimetry, regimen and the antiaging mode-of-action of the compounds under investigation. It should be noted that, apart from providing medicated feed and the taking of non-invasive samples (e.g.  cheek swabs), no other experimental procedures will be carried out on animals other than to assess their lifespan and healthspan. Manchester Metropolitan University has new labs and investments in state-of the-art technologies that the student will benefit from as well as the chance to join a thriving postgraduate student culture.         

Aims and objectives

The project, using non-invasive samples from rodents, aims to understand the mechanisms of action for longevity drugs and to refine the dosimetry and regimen required to optimise these effects. Testing of epigenetic clocks, DNA damage, immune markers and cell experiments will all be used to address these questions.

Specific requirements of the project

The applicant should have experience in molecular and cellular biology and an honours degree at first or upper second class level in a relevant subject.

The candidate should have a first or upper second class (2:1) degree in an appropriate subject. Prior experience of cell culture and common molecular biology techniques such as DNA/RNA isolation, ELISA,  PCR, QPCR, Western blotting etc would be desirable although training will be given. No prior experience of handling laboratory animals is required as this will be optional. Good IT skills with Word, PowerPoint and Excel are essential and some knowledge of statistics is desirable. 

How to apply

Interested applicants should contact Professor Chris Murgatroyd (c.murgatroyd@mmu.ac.uk) for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Life Sciences (or download the PGR application form).

You should also complete a CV (candidate’s choice of standard or Narrative CV) with a cover letter, addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 22 July 2024

Expected start date: October 2024

Please quote the reference: SciEng-CM-2024-Biological-Ageing

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is only open to home students. Home fees will be covered.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Dr Matthew Andrew (matthew.andrew@mmu.ac.uk)

Project advert

Talent identification is multifaceted and scientific research that provides evidence informed information to key stakeholders (i.e., coaches, scouts) to support their decisions regarding player identification, selection and deselection is emerging. In collaboration with Manchester United FC and Manchester Met, a three-year PhD studentship has arisen, to be embedded within the academy department, to support the male youth academy coaching and sport science staff and conduct talent identification-related research.

The successful candidate will develop audits of the current scientific information that is used to inform key stakeholders decisions on talent identification, selection and deselection. Looking at their perceptions of the effectiveness of this current information, as well as developing  and validating new tests that aim to provide information on technical, tactical and psychological skills of youth elite soccer players. The candidate will be responsible for all aspects of the research project from data collection to publication.

Aims and objectives

The aim is to better understand talent identification processes within youth male soccer at a professional soccer academy, following a multidiscipline and longitudinal approach.

This objectives are:

1. Understand the current scientific evidence that is collected/used by practitioners (e.g., coaches/scouts) that underpins the talent identification and (de)selection processes within a professional youth soccer academy.
2. Understand practitioner perceptions of potential future predicters of talent such as technical, psychological, and game intelligence skills.
3. Examine the potential validity and reliability of tests that can be used to accurately predict technical, psychological, and game intelligence skills of future expert players within a professional youth soccer academy.
4. Understand the impact of implementing testing/monitoring of future predicters to the identification and (de)selection processes within a professional youth soccer academy.

Specific requirements of the project

Essential

• First class or upper second class (2:1) degree (or equivalent) in Sports Science, Physiology, Medicine, Psychology, Coaching, or other Sports related degree
• An ability to critique and analyse scientific evidence, methodology and data
• Strong interpersonal, communication and organisational skills
• Proficient with Microsoft office, specifically Microsoft Excel

Desirable

• Experience working in soccer (e.g., Sport Scientists, Physiologists, Strength & Conditioning Coach)
• MSc or Research Masters in Sports Science or other Sports related degree
• Proficient at conducting sport science testing (e.g., physical, psychological assessments etc)
• Competent with programming / coding software (i.e., R / python etc)
   

How to apply

Interested applicants should contact Dr Matthew Andrew (matthew.andrew@mmu.ac.uk) for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Sport and Exercise Science (or download the PGR application form).

You should also complete a CV (candidate’s choice of standard or Narrative CV) with a cover letter, addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 22 July 2024

Expected start date: October 2024

Please quote the reference: SciEng-MA-2024-youth-soccer-players

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is only open to home students. Home fees will be covered.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Dr Fabio Zambolin (f.zambolin@mmu.ac.uk)

Project advert

Ageing is a complex and multifaceted phenomenon that involves a gradual decline of physiological systems and cellular processes. It is usually quantified as a measurement of the time elapsed since birth (i.e. chronological age). However, this simple count cannot explain the large variations in the ageing trajectories that exist between older people of similar age. For these reasons, researchers have tried to identify alternative descriptions of ageing based on assessments that reflect the ‘biological age’ of an individual. This was linked to the interactions of genes, environment and lifestyle choices.

The research planned within this PhD project aims to address this question of how biological age can be measured and used as a biomarker to improve the ageing process. We will develop metrics to accurately predict biological age with the longer-term goal of making the validated assessments available across very large populations of people for promoting healthy ageing. This will have an enormous impact on our society by raising the quality of life of older people living in our communities.

Aims and objectives

The project is split into three phases which include:

1. Comprehensive biomarker identification: this involves conducting a systematic review of existing literature, examining various biomarkers that have been used to assess biological age. 
2. Physiological and functional assessments of human adults: this phase of the project will establish a unique dataset by recruiting human volunteers to complete assessments in our research facilities. We will utilise advanced technology to determine mechanisms of ageing and their candidacy as biomarkers of biological age.
3. Validation of short-form assessments: advancing from the gold-standard comprehensive studies (phase two, above), the third phase of the project will validate a short-form of the assessments.

Specific requirements of the project

Ideal candidates will possess a robust background in human physiology, with a preference for those who exhibit outstanding data analysis and coding skills. A minimum of an honour’s degree at first or upper second-class (2:1) level in sport and health disciplines is required. A solid foundation in Magnetic Resonance Imaging (MRI) and experience working with elderly participants will be viewed favourably.

We seek proactive, independent, and enthusiastic individuals with a critical mindset to play a pivotal role in this cutting-edge research project. Successful applicants will become part of our research team at the Manchester Metropolitan University Institute of Sport, equipped with state-of-the-art facilities and equipment. Additionally, Manchester is recognised as one of the best cities for studying and work life balance in the UK.

How to apply

Interested applicants should contact Dr Fabio Zambolin (f.zambolin@mmu.ac.uk) for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Sport and Exercise Science (or download the PGR application form).

You should also complete a CV (candidate’s choice of standard or Narrative CV) with a cover letter, addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 22 July 2024

Expected start date: October 2024

Please quote the reference: SciEng-FZ-2024-Healthy-Ageing

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is only open to home students. Home fees will be covered.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Dr Tristan McKay (t.mckay@mmu.ac.uk)

Project advert

We are looking for a talented and motivated recent graduate in Biosciences, Biomedicine, Human Genetics or related discipline to engage in an exciting new project at Manchester Metropolitan University.

The project is focused on applying state-of-the-art tools and technologies to learn how inflammatory signals traverse the blood-brain-barrier and contribute to neurodegeneration in cerebrovascular disease. You will learn from our researchers how to apply human induced Pluripotent Stem Cells (iPSC) to convolute an in vitro chip-based complex cell model of the neurovascular unit (NVU). This NVU model will be the base to then utilise lentivirally transduced genetic reporters as quantitative readouts for neuronal and glial inflammation. You will then be instructed on applying AI/machine learning protocols to spatiotemporally quantitate these multi-fluorescence outputs in the NVU model with the aim of modelling extracellular vesicle-mediated inflammation.

The project will be supported by discipline-specific experts, and you will join a team of existing post-doctoral researchers and PhD students, with access to state-of-the-art facilities and an unrivalled environment to support our graduate students. 

Specific requirements of the project

Recent graduate in Biosciences, Biomedicine, Human Genetics or related discipline.

How to apply

Interested applicants should contact Dr Tristan McKay (t.mckay@mmu.ac.uk) for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Bioscience (or download the PGR application form).

You should also complete a CV (candidate’s choice of standard or Narrative CV) with a cover letter, addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 22 July 2024

Expected start date: October 2024

Please quote the reference: SciEng-TM-2024-Blood-Brain-Barrier

This is a full-time, 3.5 year funded PhD opportunity in the Faculty of Science and Engineering. It is only open to home students. Home fees will be covered.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Project contact

Dr Sue Anne Zollinger (s.zollinger@mmu.ac.uk)

Project advert

British waterways are in a critical state, from sewage dumping and overflows to runoff of anthropogenic chemicals from roadways, industrial discharge and agricultural land.

Water pollution can have serious consequences for wildlife, degrading aquatic ecosystems and biodiversity across many trophic levels. However, many polluted aquatic habitats, including sewage and wastewater treatment plants, are known to be profitable food sources for insectivorous songbirds. Swallows and martins are particularly reliant on these foraging sites as they often are sources of high-value prey, even as insect populations are on the decline.

This project aims to determine the extent to which endocrine disrupting chemicals (EDCs) bioaccumulate up the food chain to songbirds that rely on flying aquatic invertebrates, and to investigate how EDC levels in these aerial foragers impact their fitness, as measured by reproductive success and survival.

The project will focus primarily on sand martins (Riparia riparia) as a model system. This pioneering project will be based within Manchester Metropolitan’s Department of Natural Science, with an interdisciplinary supervisory team led by Manchester Metropolitan University in partnership with Songbird Survival and Imperial College London.

The project will deliver keystone research to establish the risks of EDCs in the waterways, invertebrates and songbirds by a combination of field sampling, nest monitoring, and environmental chemistry assays using world-leading analytical instrumentation.

Aims and objectives

This project aims to determine:

• Which endocrine-disrupting chemicals (EDCs) are present in human-altered waterways foraged by hirundine and other insectivorous songbird species, including in and around wastewater treatment plants (WWTPs)?
• Is there a credible pollution pathway for EDCs to bioaccumulate, from WWTPs to aquatic aerial invertebrates and then into hirundine and other songbird species?
• Are blood EDC levels linked to reproductive success and physiology in a model system - the sand martin (Riparia riparia)?

Specific requirements of the project

The project will involve both intensive fieldwork and laboratory work.

The successful applicant will have the ability to work in a systematic manner to collect scientifically robust data.

They should have excellent written and oral communication skills.

The project is funded by a studentship from Songbird Survival Charity, and the successful applicant will be expected to contribute to public science education via Songbird Survival’s network and outreach events.

The successful applicant with also work in close collaboration with external advisors at the UK’s Environment Agency, to support existing policy work.

The applicant will be required to obtain a home office personal animal experimentation (PIL/Avian) license, and a BTO ringing license for songbirds.

Previous experience with ringing or handling birds, and sampling aquatic invertebrates, as well as in analytical laboratory-based work, including a practical knowledge of chromatography and mass spectrometry would be an advantage.

How to apply

To apply you will need to complete the online application form for a full-time PhD in Natural Sciences (or download the PGR application form).

You should also complete a PGR thesis proposal and Narrative CV) addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date 22 July 2024.

Expected start date 1 October 2024.

Please quote the reference: SciEng-SZ-2024-DISRUPTING-CHEMICALS

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both home and overseas students. Please note that only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Please note that the expected start date for home students is October 2024 and international students January 2025.

Project contact

Dr Sean Maudsley-Barton (s.maudsley-barton@mmu.ac.uk)

Project advert

This PhD aims to develop generative models for use in the clinical assessment of movement disorders, such as Dementia, Parkinson’s and Delerium.  You will be part of a cross-departmental team at the intersection of AI and health as part of the Human Centred Computing theme.

This studentship is offered as part of wider Manchester Metropolitan University investment in our future thought leaders, this represents an opportunity to join the Faculty of Science and Engineering’s growing doctoral research community committed to excellent research with impact.  

The lack of data is a major hurdle in using AI in the assessment of movement disorders. Available datasets are small and imbalanced. Hence, the need for generative approaches to augmenting the data.  However, standard approaches can produce eery and uncanny outputs.  We propose using fundamental neuromuscular signals (EEG, EMG, ROM) to constrain and guide the generative models to produce clinically plausible data

A background in AI is essential, with links to life science, sports science or medicine desirable.  However, training will be provided to the successful candidate by our highly experienced team.

Specific requirements of the project

Essential Criteria

• Experience in training machine models with at least one of the following (Python, MatLab, R)
• An ability to critique and analyse scientific evidence, methodology and data.
• Strong interpersonal, communication and organisational skills.

Desirable Criteria

• M.Sc. or Research Masters in a relevant discipline such as mathematics, computer science, data science or statistics.
• Experience with handling EEG and/or EMG data.

How to apply

Interested applicants should contact Dr Sean Maudsley-Barton for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Computer Science (or download thePGR application form).

You should also complete the PGR thesis proposal and Narrative CV addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 22 July 2024.

Expected start date: Home students October 2024. International students January 2025.

Please quote the reference: SciEng-SMB-2024-generative-models

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both home and overseas students. Please note that only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Please note that the expected start date for home students is October 2024 and international students January 2025.

Project contact

Dr Zhihua Ma (z.ma@mmu.ac.uk)

Project advert

This studentship is offered as part of wider Manchester Met investment in our future thought leaders, this represents an opportunity to join the Faculty of Science and Engineering’s growing doctoral research community committed to excellent research with impact.

Ocean waves are crucial in engineering and environmental sciences. Energetic waves can severely damage seawalls, ships, oil/gas platforms and marine renewable energy infrastructure. More broadly, ocean waves also influence the earth’s climate, weather, and eco-systems by facilitating the mass and heat exchange across the air-sea interface. Accurate estimate of mass and heat transfer in waves is of utmost importance in: 1) formulating more accurate weather forecasting models, and 2) better predicting extreme loadings on marine structures.

Numerical modelling provides a feasible way to overcome the limit of theoretical methods and measurement technologies to acquire the needed fine-scale data to investigate the fundamental physics.

The project will build on the Mathematical Modelling and Flow Analysis Group’s most recent research achievement to develop a new-generation interdisciplinary physical-engineering-environmental numerical modelling framework to investigate mass and heat transfer in energetic ocean waves.

Project aims and objectives

1. Develop an interdisciplinary physical-engineering-environmental numerical framework for modelling mass and heat transfer in ocean surface waves.
2. Determine the roles of fluid diffusivity and wave motion in mass and heat transfer under still water and non-breaking monochromatic wave conditions.
3. Determine wave steepness effects on mass/heat transfer rate under various wave conditions.
4. Determine heat penetration depth in water columns for wave groups subject to modulation instability under non-, weakly- and strongly-breaking conditions.

Specific requirements of the project

Applicants should hold, or expect to receive, a First Class or high Upper Second Class Honours degree (or the equivalent) in in Physics, Engineering or Mathematics.

A master’s level qualification in Applied/Computational Mathematics, Engineering or similar would be desirable.

Experiences in one or several of the following areas would be beneficial:

• Computational Fluid Dynamics,
• Hydrodynamics and Thermodynamics,
• Numerical modelling of heat transfer.

How to apply

Interested applicants should contact Dr Zhihua Ma for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Mathematics (or download the PGR application form).

You should also complete the PGR thesis proposal and Narrative CV addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 22 July 2024.

Expected start date: Home students October 2024. International students January 2025.

Please quote the reference: SciEng-ZM-2024-ocean-surface-waves

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both home and overseas students. Please note that only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Please note that the expected start date for home students is October 2024 and international students January 2025.

Project contact

Dr Wenqi Lu (w.lu@mmu.ac.uk)

Project advert

This studentship is part of Manchester Metropolitan University’s investment in future thought leaders and offers an opportunity to join the Faculty of Science and Engineering’s growing doctoral research community focused on impactful research.

Aortic valve stenosis (AS) causes significant morbidity and mortality, with severe cases increasing the risk of heart failure, syncope, and sudden cardiac death. Echocardiography and CT are key for assessing AS severity, but conventional methods are time-consuming and susceptible to intra- and inter-observer variations. Current AI models use single modality inputs, failing to effectively integrate data from multiple sources due to their heterogeneity.

This project aims to develop a novel solution for accurate AS diagnosis and prognostication by employing transformer-based AI techniques as the disease classifier and survival predictor using data from multiple clinical domains. We partner with clinicians to provide clinical advice and relevance of our model development and validation.

Our outputs will enhance workflow processes and reduce diagnostic variability. Multi-modal AI can improve patient outcomes by enabling earlier detection of AS and better risk stratification for valvular intervention, leading to more personalised treatments. This aligns with our Human-centred Computing research theme, which focuses on creating technologies to enhance human capabilities and improve quality of life.

Project aims and objectives

The proposed research project aims to develop a novel solution for accurate AS diagnosis and prognostication by employing multi-modal AI techniques. The research objectives are to:

1. automate the analysis and reporting of electrocardiography, echocardiography, and CT images to derive biomarkers in AS patients
2. autonomously classify AS severity and subtype
3. improve patient outcomes by facilitating risk stratification for interventions such as valve replacement surgery.

The proposed project aligns closely with our faculty’s Human-centred Computing research theme, which focuses on developing technologies that enhance human capabilities and improve quality of life. This project addresses a critical healthcare need while exemplifying Human-centred Computing principles by enhancing patient care through advanced, user-friendly, and personalised AI technologies.

Specific requirements of the project

Successful candidates would have a strong background in Computer Science, Engineering, Maths or Physics, and preference would be given to those with a good understanding of computer vision and deep learning.

It is essential to have a good background knowledge of machine learning and computer programming and a proactive approach to their work.

How to apply

Interested applicants should contact Dr Wenqi Lu (w.lu@mmu.ac.uk) for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Computing and Digital Technologies (or download the PGR application form).

You should also complete the PGR thesis proposal and Narrative CV addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

Applicants should ensure their submitted CV clearly demonstrates any experience and work in ML and AI

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 22 July 2024. 

Expected start date: Home students October 2024. International students January 2025.

Please quote the reference: SciEng-CS-2024-aortic-valve-stenosis

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both home and overseas students. Please note that only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Please note that the expected start date for home students is October 2024 and international students January 2025.

Project contact

Dr Yakubu Tsado (y.tsado@mmu.ac.uk)

Project advert

This studentship is offered as part of wider Manchester Metropolitan University’s investment in our future thought leaders, this represents an opportunity to join the Faculty of Science and Engineering’s growing doctoral research community committed to excellent research with impact.

In a world where cyber threats are constantly evolving, where standards and frameworks are maturing, one critical factor remains: human behaviour. From Edward Snowden’s historic NSA leak to the shocking statistic that 95% of cyber attacks succeed due to human error, it’s clear that human behaviour is crucial to safeguarding organisational security.

This multi-disciplinary project will give you the opportunity to contribute to cutting-edge research aimed at investigating the intersection of computing and psychology. Your role will involve designing and executing experiments, analysing data, and developing novel techniques informed by behavioural science to address human-centric cyber threats across various industries.

We seek motivated individuals with a passion for research and a drive to advance cyber risk management to be part of a collaborative team dedicated to driving innovation to make a difference in the world of cyber security. 

Project aims and objectives

The aim of the project is to develop processes, tools, and techniques to combat the growing socio-technical risks to information security across various industries. The main objectives include:

1. Identifying the extent of the problem through a blended approach of quantitative and qualitative methods to explore why industries find this challenge overwhelming and struggle to implement effective techniques.
2. Investigating cognitive behaviour patterns such as ‘when’ and ‘why’ people engage in certain adverse behaviours by analysing conscious thoughts, motivations, beliefs, social influence, contextual effects (e.g., workplace surroundings), and habits.
3. Proposing new procedures and policies, considering ethical considerations and leveraging advances in technology (e.g., AI), to assist in mitigating this specific risk.

Specific requirements of the project

Essential 

• The candidates should have a strong research background in information security, cyber risk management, security policies and procedures.
• A master’s degree in computer science, Information Technology, Cyber Security, or a related field is preferred.

Desirable

• Proficiency in cognitive behavioural analysis techniques and methodologies.
• Experience with data analysis tools and programming languages, such as python, R, or Matlab
• Industry experience in cyber security/risk management and knowledge of cyber-physical systems are highly desirable.
• In-depth knowledge of regulatory frameworks and standards in cyber security, such as NIST standards, GDPR and HIPAA

How to apply

Interested applicants should contact Dr Yakubu Tsado (y.tsado@mmu.ac.uk) for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Computer Science (or download the PGR application form).

You should also complete the PGR thesis proposal and Narrative CV(supplementary information) form addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 22 July 2024. 

Expected start date: Home students October 2024. International students January 2025.

Please quote the reference: SciEng-YT-2024-cyber-risk-management

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both home and overseas students. Please note that only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Please note that the expected start date for home students is October 2024 and international students January 2025.

Project contact

Dr Misbahu Zubair (m.zubair@mmu.ac.uk)

Project advert

This studentship is offered as part of wider Manchester Metropolitan University’s investment in our future thought leaders, this represents an opportunity to join the Faculty of Science and Engineering’s growing doctoral research community committed to excellent research with impact.

We are inviting applications for a PhD studentship focused on the design, development and efficacy of adaptive serious games that leverage Generative AI for dynamically adapting content to meet the unique needs and interests of individuals with Learning Disabilities (LD).

Serious games are used to teach several skills to individuals with LD including social and independent living skills. However, designing and developing serious games accessible to all members of the population is a complex challenge due to diverse and unique characterises and interests.

The successful candidate will develop a framework for co-designing serious games that leverage Generative AI for dynamic content adaptation through a co-design process with individuals with LD and stakeholders; develop prototypes of serious games for teaching independent living skills to young adults with LD that support dynamic content adaptation using Generative AI; and evaluate the prototypes with the target group.

The successful candidate will work with a team of experts who will support and guide them through the project.

Project aims and objectives

This research aims to: Explore the design and evaluate the efficacy of adaptive serious games that leverage generative AI for dynamically generating and adapting content to meet the needs of individuals with Learning Disabilities (LD).

The aim will be met by achieving the following objectives:

• Complete an extensive literature review to understand existing knowledge in the field.  
• Develop a novel framework for co-designing serious games that leverage generative AI for dynamic content adaptation and generation aimed at improving the personalization of player experiences.
• Co-design serious games for teaching independent living skills to young adults with LD.
• Prototype serious games for teaching independent living skills to young adults with LD.
• Evaluate the prototype(s) through a user study with young adults to understand and measure the effects of Generative AI content adaptation on player experience and learning efficacy.
• Disseminate findings of the project through at least 3 publications across different stages of the project.

Specific requirements of the project

The successful candidate must evidence competency in games development and hold a first-class/2:1 degree in games development, games design or a related computer science discipline.

The candidate will be expected to produce the evidence in their application and at an interview. There is no expectation that the candidate has published a game or has worked on a published game, though this would be advantageous.

The candidate is also expected to have a passion for serious games and evidence of a strong understanding of the area. Knowledge of accessibility, inclusive design, and Generative AI is not a requirement, it will however be beneficial.

The ideal candidate will also possess the following qualities:

• Masters degree in games development, games design or a related computer science discipline
• Have an ability to learn and apply new concepts quickly.
• Have strong time-management skills.
• Show an understanding of project management.
• By proficient in either C# or C++
• Have knowledge of the Unity or Unreal Game engine
• Be independent in their work.
• Be open to constructive criticism.
• Possess excellent communication and collaboration skills.

How to apply

Interested applicants should contact Dr Misbahu Zubair at m.zubair@mmu.ac.uk for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Computer Science (or download the PGR application form).

You should also complete the PGR thesis proposal and Narrative CV addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: 22 July 2024. 

Expected start date: Home students October 2024. International students January 2025.

Please quote the reference: SciEng-MZ-2024-adaptive-serious-games

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both home and overseas students. Please note that only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Please note that the expected start date for home students is October 2024 and international students January 2025.

Project contact

Dr Olamide Jogunola (o.jogunola@mmu.ac.uk)

Project advert

This studentship is offered as part of the wider Manchester Metropolitan University’s investment in our future thought leaders, this represents an opportunity to join the Faculty of Science and Engineering’s growing doctoral research community committed to excellent research with impact.

The introduction of large-scale quantum computing will profoundly affect industry, economy and society. It will bring opportunities such as rapid computation of large, unsorted data sets, rapid training of AI models, higher capacity Lithium-ion batteries.

With these profound opportunities comes the security threat of quantum computing, including the possibility of hacking computationally secure cryptographic algorithms, such as RSA and hash encryption. An example of these threats is in the blockchain network that is gaining momentum in decentralised sectors and trustless environments, such as supply chain, health care records, energy network.

While these quantum threats and potential solutions are been established in the literature, most proposals are theoretical and require large quantum key distribution networks or a new build-up of quantum-resistant blockchain networks. These methods are not fit for purpose.

Thus, this research aims to accelerate the UK’s secure blockchain quantum resistance by developing a framework for designing post-quantum blockchain and distributed ledger technologies applicable to existing blockchain networks for quantum-safe properties.

Project aims and objectives

This project is in the remit of Manchester Met’s research theme on AI, Digital and Cyber Physical Systems.

The project aims to develop a framework for the design of a quantum-resistant blockchain applicable to the current blockchain network infrastructure.

This will help to secure the asymmetric encryption used for digital signatures in several distributed systems against quantum and cyberattacks.

The output of this project will have a profound impact on people, businesses, technologies, and the economy, as well as policy impact on regulations on the designs and standards for blockchain-resistance quantum technology.

Specific requirements of the project

• Minimum of an upper second class in Computer Science or related discipline.
• An MSc in Computer Science or related field will be an added advantage.
• A keen interest in quantum computing and blockchain.
• Prior experience with programming languages, such as Python, Solidity, etc.
• Prior experience with quantum software platforms, such as IBM Qiskit, Quantum Virtual Machine, or similar platforms.
• Knowledge of cryptographic algorithms, such as Rivest-Shamir-Adleman (RSA) and hash encryption.

How to apply

Interested applicants should contact Dr Olamide Jogunola for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Computing and Digital Technologies (or download the PGR application form).

You should also complete the PGR thesis proposal and Narrative CVaddressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk. 

Closing date: 22 July 2024. 

Expected start date: Home students October 2024. International students January 2025.

Please quote the reference: SciEng-OJ-2024-quantum

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both home and overseas students. Please note that only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Please note that the expected start date for home students is October 2024 and international students January 2025.

Project contact

Dr Xinqi Fan (x.fan@mmu.ac.uk)

Project advert

This studentship is offered as part of wider Manchester Metropolitan University’s investment in our future thought leaders, this represents an opportunity to join the Faculty of Science and Engineering’s growing doctoral research community committed to excellent research with impact.

This project forms an exciting research opportunity to join Manchester Met’s AI, Digital and Cyber-Physical Systems theme for improving life quality. Homecare robots are seen as a solution to the challenges posed by the rapidly growing elderly population and increasing healthcare costs.

However, most robots only offer physical assistance and lack emotional support. This project aims to address these limitations by building multimodal emotion understanding methods with an application to human-robot interactions to understand and response to human emotions.

The successful candidate will join the energetic human-centred computing group led by Professor Moi Hoon Yap.

Project aims and objectives

The project aims to design innovative deep multimodal learning methods to understand human emotions. It will address the following key questions:

1. How can large-scale unlabelled videos be effectively utilized?
2. How can privacy concerns from humans be appropriately addressed?
3. How can true emotions be detected when individuals conceal them?
4. How to integrate multimodal emotion understanding algorithms on a robot?

Specific requirements of the project

Candidates must have a strong motivation for research and excellent programming skills. Expertise of developing computer vision and machine learning algorithms would be desirable, with an interest in image analysis.

Qualifications

• A high-grade undergraduate degree (first class or upper second) in Computer Science or MSc in related field

Skills

• Knowledge of software development and programming
• Good communication and writing skills
• Developing image analysis/machine learning algorithms would be beneficial
• Able to work as part of a joint team for deep learning and robotics

How to apply

Interested applicants should contact Dr Xinqi Fan (x.fan@mmu.ac.uk) for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Computing and Digital Technologies (or download the PGR application form).

You should also complete the PGR thesis proposal and Narrative CV addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk. 

Closing date: 22 July 2024. 

Expected start date: Home students October 2024. International students January 2025.

Please quote the reference: SciEng-XF-2024-Multimodal-Emotion-Understanding

This is a full-time, funded PhD opportunity in the Faculty of Science and Engineering. It is open to both home and overseas students. Please note that only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Please note that the expected start date for home students is October 2024 and international students January 2025.

Project contact

Dr Sergio Davies (sergio.davies@mmu.ac.uk)

Project advert

This studentship is offered as part of wider Manchester Metropolitan University’s investment in our future thought leaders, this represents an opportunity to join the Faculty of Science and Engineering’s growing doctoral research community committed to excellent research with impact.

This groundbreaking research project addresses the pressing need for enhanced network security in our increasingly connected world. As artificial intelligence (AI) and interconnectivity continue to reshape industries, the demand for robust network security has never been more critical.

Traditional methods struggle with real-time network traffic, especially on lightweight, low-power, resource-constrained edge devices. This project aims to explore the potential of spiking neural networks (SNNs) to revolutionize intrusion detection systems (IDSs) in such devices.

Inspired by the efficiency of biological neural networks, this project offers a unique opportunity to pioneer real-time threat detection solutions. By developing and training an innovative SNN architecture specifically tailored for IDS applications, this project aims at advancing AI, neuromorphic computing, and cybersecurity domains.

Aligned with our University’s strategic AI, Digital, and Cyber Physical Systems theme, this research will bolster our excellence and impact in these vital areas. Join us in shaping the future of network security and contributing to cutting-edge advancements in AI and digital technology.

Project aims and objectives

1. Revolutionise Network Security: Leverage the rapid growth of AI and explore how Spiking Neural Networks (SNNs) can enhance intrusion detection systems (IDSs) to ensure safer network communications.
2. Enhance Lightweight Devices: Address the challenges faced by lightweight, low-power, and resource-constrained edge devices in handling real-time network traffic.
3. Advance Biologically-Inspired Computing: Drawing inspiration from biology, this project pioneers a cutting-edge platform for streamlined and efficient computing.

Specific requirements of the project

Candidates must have a strong motivation for research and excellent programming skills. Expertise of Spiking Neural Networks and of their training methodologies would be desirable, and with an interest in computer network traffic analysis.

Qualifications

• A high grade undergraduate degree (first class or upper second) in Computer Science
• A MSc level in Computer Science or related field would be desirable for this post

Skills

• Knowledge of software development and programming (ideally in python or C/C++)
• Knowledge of neural networks and machine learning algorithms would be beneficial
• Good communication and writing skills
• Self-motivated to conduct research activities independently
• Able to work as part of a joint academia and industry team

How to apply

Interested applicants should contact Dr Sergio Davies for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Computing and Digital Technologies (or download the PGR application form).

You should also complete the PGR thesis proposal  and Narrative CV  addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk. 

Closing date: 22 July 2024. 

Expected start date: Home students October 2024. International students January 2025.

Please quote the reference: SciEng-SD-2024-intrusion-detection