CLOSED PhD studentships

Annual stipend: UKRI stipend rate (plus London wage).

Start date: September 2024 (Monday 16th if not visa delay).

Supervisors: Dr. Charalampos Saitis, Dr. George Fazekas in collaboration with Yamaha.

Abstract: There is evidence that production techniques like equalisation (EQ) can significantly influence a listener's emotional response to music. In professional music production, precise EQ adjustments are used to evoke specific emotional effects. However, in consumer audio, users often struggle to achieve the desired sound quality, leading to a diminished music-listening experience and a pervasive sense of frustration. Ideal EQ settings are highly personal and context-dependent, with no universal solution. Many users find it challenging to achieve the sound they desire due to diverse individual preferences and situational contexts. Some prioritise content, while others focus on sound quality. Psychological factors such as introversion versus extroversion and individual sensitivity also influence preferred EQ settings. These preferences fluctuate with mood, environment, and specific listening contexts, adding another layer of complexity.

The goal of this PhD is to create a model predicting ideal EQ settings for individuals based on their unique preferences and contextual factors. This involves conducting experiments with a diverse group of listeners to gather data on EQ preferences across various contexts. Semantic descriptors related to EQ settings will be collected via crowdsourcing, along with psychological traits and contextual information. Using this rich dataset, a predictive model will be developed with state-of-the-art machine learning techniques, incorporating insights from psychological and contextual factors to enhance accuracy. Validation will be an iterative process involving rigorous testing and refinement. Collaboration with Yamaha will aim to integrate the model into a consumer audio system capable of real-time adaptive EQ adjustments, followed by user testing to evaluate and refine the system. Research by Dourou (2022) found that listeners with low arousal levels prefer EQ settings that boost lower frequencies, aligning with observations in music production and highlighting the need for personalised EQ. Stables et al. (2016) provide a framework for understanding terms and processes to achieve desired timbral effects, informing context-aware EQ development. Further inspiration comes from multimodal models for music and language (Manco et al., 2022) and crowdsourcing for semantic descriptors (Cartwright and Pardo, 2014) as well as other works listed in the references below.

The ideal candidate will have an interest in music technology, music emotion research, signal processing, machine learning as well as the latest deep learning techniques. Basic understanding of music theory is useful but not essential.

How to Apply - https://aim.qmul.ac.uk/apply/

Primary supervisor: Dr George Fazekas and Dr Charalampos Saitis (C4DM) Email: george.fazekas@qmul.ac.uk; c.saitis@qmul.ac.uk

AIM CDT Website: UKRI Centre for Doctoral Training in Artificial Intelligence and Music (qmul.ac.uk)

C4DM Website: Home Page (qmul.ac.uk)

Annual stipend: £21,237

Application closing date: 28 July 2024

Interviews: Interviews to take place on 5-6 September 2024 (Online)

Start date: September 2024

Up to 4 PhD fully funded PhD studentships are available in the Keystone project on addressing socio-technical limitations of Large Language Models (LLMs) in medical and social contexts. The PhD project will be part of AdSoLve, a large multi-disciplinary consortium funded by UKRI and RAi UK as part of strategic investment by the UK Government to create an international ecosystem for responsible AI research and innovation. It is led by Prof Maria Liakata at QMUL, with four University partners (QMUL, Nottingham, Sheffield and Warwick) and 21 external partners, including large, diversified companies, NHS trusts, NHS England and AI hubs and UKRI and EPSRC Centres for Doctoral Training (CDTs).

The successful applicants will be registered either at QMUL with a co-supervisor at Imperial College London or the UKRI AI Centre for Doctoral Training in Digital Healthcare (AI4Health) at Imperial College London with a co-supervisor from QMUL, depending on project/topic suitability. The studentships will cover UK tuition fees and stipends at UKRI rate (£21,237 for 2024-25) for a minimum of 3 years subject to successfully completing progression milestones.

Context

The vision of the AdSoLve consortium is to address the socio-technical limitations of LLMs that challenge their responsible and trustworthy use, particularly in the context of medical and legal use cases.

AdSoLve has two primary goals:

1. To create an extensive evaluation benchmark (including suitable novel criteria, metrics and tasks) for assessing the limitations of LLMs in real world settings, enabling our standards and policy partners to implement responsible regulations, and industry and third sector partners to robustly assess their systems. To achieve this synergy we will be running co-creation and evaluation workshops throughout the project to create a co-production feedback loop with our stakeholders.
2. Secondly, is to devise novel mitigating solutions based on new machine learning methodology, informed by expertise in law, ethics and healthcare, via co-creation with domain experts, that can be incorporated in products and services. Such methodology includes development of modules for temporal reasoning and situational awareness in long-form text, dialogue and multi-modal data, as well as alignment with human-preferences, bias reduction and privacy preservation.
    

The PhD research area

This PhD project will focus on the intersection of natural language processing (NLP), machine learning (ML), multimodal AI, responsible innovation, and healthcare. The successful applicant will be expected to work on one or two of the following topics during their PhD:

• State-of-the art methods for efficient and robust alignment of LLMs with human preferences for reliable generation and reduction in hallucinations
• Robust methods, tasks and metrics for fine-grained evaluation of generated content
• Augmentation of LLMs with temporal reasoning and situational awareness
• Fine-grained alignment of cross-modal LLMs
• Multi-modal representation learning for longitudinal settings
• Methods for longitudinal multi-modal monitoring for healthcare
• Methods for longitudinal synthetic language generation for data augmentation and privacy preservation.
• Methods for situation-aware, privacy-preserving, trustworthy dialogue systems for longitudinal health monitoring and self-management
• Privacy preservation and bias reduction while applying LLMs to sensitive data
• Explainable prediction and generation with LLMs
• Methods for temporally aware summarisation of real-world long-form documents including therapy sessions, social media threads, court cases.

Desired outputs include (but are not limited to) publications in top-tier NLP and ML venues and are expected to have high impact in the fields of NLP, machine learning, as well as domains such as healthcare and law, both in terms of methodological innovation and their application to real-world settings. There will also be the opportunity to work closely with researchers at the Alan Turing Institute.

The AdSoLve Keystone project and UKRI AI Centre for Doctoral Training in Digital Healthcare (AI4Health Centre)

The AdSoLve Keystone project is a large multi-disciplinary consortium project funded by UKRI and RAi UK. Details can be found at https://adsolve.github.io/).

The UKRI AI Centre for Doctoral Training in Digital Healthcare (AI4Health CDT) at Imperial College London delivers cohort-based training that integrates the development of technical skills with an appreciation for approaches to human-in-the-loop AI design that are socially and ethically acceptable. Details can be found at https://ai4health.io.

Eligibility

Essential Qualifications

• Academic degrees:
• [MSc or 4-year UG degrees] with Distinction and/or first-class honours in [Natural Language Processing, Computer Science, Machine Learning, Artificial Intelligence, Engineering or related field]. Exceptional candidates with a background in Medicine, Psychology, Psychiatry or Law may also be considered.
• Experience in at least one of the areas below:
• Natural Language Processing Machine Learning/Deep Learning
• Programming Skills:
• Strong coding skills, preferably in Python.

Desirable skills

• Programming skills:
• Experience with machine learning frameworks based on Python such as Pytorch, TensorFlow and libraries from Huggingface
• Familiarity with python scientific packages e.g. numpy, pandas, sklearn, scipy, matplotlib
• Experience with version control systems
• Research Experience:
• Experience with working in research projects
• Publication(s) in NLP/ML venues
• Interest in/ Knowledge of Health applications & associated challenges
• Interest in learning about new AI technologies

We particularly encourage students from groups that are currently underrepresented in postgraduate science research, including black and minority ethnic students and those from a socio-economically disadvantaged background.

Application Information, Studentship and Eligibility

The studentship covers:

• Full-time PhD tuition fees at the UK home rate. For successful international candidates it may be possible to cover international fees.
• An annual (usually tax free) stipend set at the UKRI rate (£21,237 for 2024-25) per year, for a minimum of 3 years, subject to satisfying progression milestones.

To be classed as Home for tuition fee purposes, students typically need to have unrestricted access on how long they can remain in the UK (i.e. are a British National, have settled, or pre-settled status, have indefinite leave to remain etc.). The tuition fee status is determined by the university’s Registry at the point of application.

How to apply

• Application Process: Applicants are invited to submit their applications through the Queen Mary University of London Application system Subjects - Queen Mary University of London (qmul.ac.uk). Scroll down and select PhD Full Time Computer Science - Semester 1 (September Start).
• Successfully shortlisted applicants considered for a project with the supervisor from Imperial would be advised to apply through the Imperial College application system, and satisfy the admissions requirements of both Imperial College and the PhD programme of the UKRI AI Centre for Doctoral training in Digital Healthcare (AI4Health).
• All applicants must submit the following:
• 4-year UG degree/Diploma/PG Degree transcripts (translated in English, if needed)
• CV (max 2 pages)
• A one-side A4 statement of purpose
• Research proposal (500 words)
• 2 References
• Certificate of English Language (for non-UK students)
• Other (academic or not) Certificates

Queries regarding the PhD topic and project should be directed to Prof Maria Liakata (m.liakata@qmul.ac.uk)

If there are any queries about the UKRI AI Centre for Doctoral Training in Digital Healthcare, please contact Britta Ross b.ross@imperial.ac.uk

Deadline for Applications: 28 July 2024 (23:59GMT)

Applications Open: early December - late January every year until 2023.  Start date: Sept 2023 Research group: Centre for Doctoral Training in Artificial Intelligence and Music (AIM)

Duration: 4 years Funding available

School of Electronic Engineering and Computer Science
4-year PhD Studentship in Artificial Intelligence and Music

Each year at least 12 fully funded PhD positions are available in the UKRI Centre for Doctoral Training in Artificial Intelligence and Music (AIM). For more information on available topics, please visit https://www.aim.qmul.ac.uk/apply. 

• 4-year fully-funded PhD studentship
• Access to cutting-edge facilities and expertise in artificial intelligence (AI) and music/audio technology
• Comprehensive technical training at the intersection of AI and music through a personalised programme
• Partnerships with over 20 companies and cultural institutions in the music, audio and creative sectors

More information on the AIM Programme can be found at: www.aim.qmul.ac.uk.

AIM Programme structure

Our Centre for Doctoral Training (CDT) offers a four-year training programme where students will carry out a PhD in the intersection of AI and music, supported by taught specialist modules, industrial placements, and skills training. Find out more about the programme structure at: www.aim.qmul.ac.uk/about.

Who can apply?

We are on the lookout for students interested in the intersection of music/audio technology and AI. Successful applicants will have the following profile:

• Hold or be completing a Masters degree at distinction or first class level, or equivalent, in Computer Science, Electronic Engineering, Music/Audio Technology, Physics, Mathematics, or Psychology. We also accept applicants holding or completing a Masters degree at distinction or first class level, or equivalent, in Music Performance/Composition or Musicology, provided that the applicant has a suitable technical background to undertake a PhD in AI and Music.
• Programming skills are strongly desirable; however, we do not consider this to be an essential criterion if candidates have complementary strengths.
• Music training (any of performance, production, composition or theory) is desirable but not a prerequisite. 

For the above PhD topic, we are offering a scholarship to both ordinarily resident in the UK and international students.

Funding

This is a fully-funded 4-year PhD studentship with 5 cohorts starting in September 2019-20-21-22 and 2023 which will cover the cost of tuition fees and will provide an annual tax-free stipend of 19,668 (in 2022-23 academic year). The CDT will also provide funding for conference travel, equipment, and for attending other CDT-related events.

Apply Now

Information on applications and PhD topics can be found at: http://www.aim.qmul.ac.uk/apply

Applications Open

Call open to all applicants - application deadline January to enter in September. UK resident calls check the website.

About the Studentship

Queen Mary University of London is inviting applications for the DeepMind PhD Studentship for September 2023. 

The DeepMind PhD Studentship programme is established at Queen Mary University of London in partnership with leading British AI company, DeepMind. 

The PhD Studentship supports and encourages under-represented groups, namely female and Black researchers, to pursue postgraduate research in AI or Machine Learning. 

The PhD DeepMind Studentship will cover tuition fees and offer a London weighted stipend of £19,668 per year minimum together with an annual £2,200 travel and conference allowance and a one-off equipment grant of £1,700.

• 3-year fully-funded PhD Studentship
• Access to cutting-edge facilities and expertise in AI
• Partnership and mentorship with DeepMind employees working at the cutting edge of AI research and technologies.

Who can apply
Queen Mary is on the lookout for the best and brightest students in the fields of AI and Machine Learning. 

Successful applicants will have the following profile:

• Identify as female and/or are of Black ethnicity, each being under-represented groups in the field of Artificial Intelligence and Computer Science
• Should hold, or is expected to obtain an MSc in Computer Science, Electronic Engineering, AI, Physics or Mathematics or a closely related discipline; or can demonstrate evidence of equivalent work experience
• Having obtained distinction or first-class level degree is highly desirable
• Programming skills are strongly desirable; however, we do not consider this to be an essential criterion if candidates have complementary strengths. 

We actively encourage applications from candidates who are ordinarily resident in the UK. The studentship is also open to International applicants. 

About the School of Electronic Engineering and Computer Science at Queen Mary

The PhD Studentship will be based in the School of Electronic Engineering and Computer Science (EECS) at Queen Mary University of London. As a multidisciplinary School, we are well known for our pioneering research and pride ourselves on our world-class projects. We are 8th in the UK for computer science research (REF 2021) and 7th in the UK for engineering research (REF 2021). The School is a dynamic community of approximately 350 PhD students and 80 research assistants working on research centred around a number of research groups in several areas, including Antennas and Electromagnetics, Computing and Data Science, Communication Systems, Computer Vision, Cognitive Science,  Digital Music, Games and AI, Multimedia and Vision, Networks, Risk and Information Management, Robotics and Theory

For further information about research in the school of Electronic Engineering and Computer Science, please visit: http://eecs.qmul.ac.uk/research/.

How to apply

Queen Mary is interested in developing the next generation of outstanding researchers - whether in academia, industry or government – therefore the project undertaken under this Studentship is expected to fit into the wider research programme of School. Applicants should select a supervisor (a first and second choice) from the School at application stage. Visit our website for information about our research groups and supervisors: eecs.qmul.ac.uk/phd/phd-opportunities/

Applicants should submit their interest by returning the following to ioc@qmul.ac.uk by 12pm (noon), 10 April 2023:

• Indicate first and second choice academic supervisor 
• CV (max 2 pages) 
• Cover letter (max 4,500 characters)
• Research proposal (max 500 words) 
• 2 References 
• Certificate of English Language (for students whose first language is not English) 
• Other Certificates  

Application deadline: 10 April 2023

Applications will be reviewed by a panel of academic staff: May 2023

Interviews: April/May 2023

Start date: September 2023

 

Agile Microwave Circuits and Antennas Made Using Liquids 

Annual stiped: £20,622

Application closing date: Ongoing

Start date: January 2024

Energy-efficient electric propulsion systems for marine vessels and heavy-duty road vehicles

Annual stipend: £20,622

Start date: January 2024, but can be negotiated

The project is to implement energy-efficient electric propulsion systems for marine vessels and heavy-duty road vehicles. These vessels are power intense, so the powertrain must respond quickly to sharp changes in power demands in an uncertain environment. This project will seek to develop a systematic globally optimum cyber-physical design for the energy-efficient propulsion system and elevate the current design approach for pre-existing electric powertrains and the associated energy management system.

The project is industrially assigned and requires a strong background in various specialised areas within electric propulsion design, electric drive systems, power electronics, control systems, machine learning and deep reinforcement learning. Experience in building electric powertrains, power electronics test rigs, and associated practical skills in hardware-in-the-loop control systems is mandatory.

The PhD project is funded by EPSRC DTP studentships open to those with Home and International fee status; however, the number of students with international fee status who can be recruited is capped according to the EPSRC terms and conditions, so competition for international places is particularly strong.

How to apply: http://www.eecs.qmul.ac.uk/phd/how-to-apply/ 

Primary supervisor: Dr Kamyar Mehran (EECS)

Email: k.mehran@qmul.ac.uk

RPCS Laboratory Website: https://kamyarmehran.eecs.qmul.ac.uk/

Supervisor: Dr Marc Roth

Project Description 

“Motif Counting” describes a family of computational problems that arise in the context of large-scale network analysis, and that have found numerous applications in datamining, bioinformatics, genetics, and artificial intelligence. More concretely, an instance of a motif counting problem is a pair of a small pattern (called the motif) P, and a large network N, and the task is to compute the number of occurrences of P in N. For example, if P is a triangle, then the corresponding motif counting prob-lem is essentially equivalent to the computation of the global clustering coefficient. Recent years have shown remarkable progress in our theoretical understanding of the computational complexity of those problems, including implications for the theoretical limitations of the expressive power of graph neural networks. However, most existing results apply only for graph-like data, and thus not for data organised in higher arity relations such as provided in (relational) database systems. This project aims to address this gap by investigating the complexity and the expressive power of Motif Counting Problems that arise in higher order structures. Concretely, the objectives of the project are:

1. Identification of Motif Counting Problems on Higher Order Structures: Which higher order motif counting problems are already used in practice, but computationally too costly? Which patterns have the potential to describe global properties of data organised in higher arity re-lations?

2. Complexity Analysis of Motif Counting Problems for patterns identified in 1.: Using mod-ern algorithmic toolkits and lower bound techniques such as parameterised algorithmics and fine-grained complexity theory, what are the theoretically best algorithms for solving those problems?

3. Approximation Algorithms for Hard Motif Counting Problems: Design of efficient approxi-mation algorithms for Motif Counting Problems that have been established to be hard in 2. This can include the application and adaptation of well-established tools in classical approxi-mation algorithm engineering such as Markov-Chain-Monte-Carlo approaches, as well as the development of new tools tailored to Motif Counting Problems.

4. Descriptive Complexity Theory and (Hyper-)Graph Neural Networks: Analysis of the ex-pressive power required to model Higher Order Motif Counting problems in selected exten-sions of first-order logic that incorporate the ability to “count”. Translation of the logical characterisation to the study of the theoretical limitations of the expressiveness of Higher Order Graph Neural Networks or “Hypergraph Neural Networks”.

The successful candidate will work on some of the above objectives, and there will also be opportu-nities to work on new (but related) directions. The candidate is expected to have a strong back-ground in the design and analysis of algorithms, as well as in graph theory. Prior knowledge in one or more of the following topics is advantageous: Parameterised Algorithms / Fine-grained Complexity Theory / Randomised and Approximation Algorithms / Descriptive Complexity Theory / Graph Neural Networks. 

The PhD student will receive tuition fees and a London stipend at UKRI rates (currently in 2024/25 of £21,237 per year, to be confirmed for 2025/26) annually during the PhD period, which can span for 3 years.

For more information about the project, please contact Marc Roth.

Supervisor
Dr Marc Roth (he/his) – m.roth@qmul.ac.uk

Centre for Fundamental Computer Science
Personal Homepage 
Google Scholar

How to apply 

Queen Mary is interested in developing the next generation of outstanding researchers and decided to invest in specific research areas.  For further information about potential PhD projects and super-visors please see the list of the projects at the end of this page.
Applicants should work with their prospective supervisor and submit their application following the instructions at: http://eecs.qmul.ac.uk/phd/how-to-apply/   
The application should include the following: 

•    CV (max 2 pages)  
•    Cover letter (max 4,500 characters) stating clearly in the first page whether you are eligible for a scholarship as a UK resident (https://epsrc.ukri.org/skills/students/guidance-on-epsrc-studentships/eligibility)   
•    Research proposal (max 500 words) 
•    2 References  
•    Certificate of English Language (for students whose first language is not English)  
•    Other Certificates  

Please note that to qualify as a home student for the purpose of the scholarships, a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship.  For more information please see: (https://epsrc.ukri.org/skills/students/guidance-on-epsrc-studentships/eligibility)  

Application Deadline 
The deadline for applications is the 3rd January 2025. 

For general enquiries contact Mrs Melissa Yeo at m.yeo@qmul.ac.uk (administrative enquiries) or Dr Arkaitz Zubiaga at a.zubiaga@qmul.ac.uk (academic enquiries) with the subject “EECS 2025 PhD scholarships enquiry”. 

For specific enquiries contact Dr Marc Roth at m.roth@qmul.ac.uk

NDA Industrial-Funded PhD Studentship: Wireless Powered LoRa Through-Wall Indoor Nuclear (LoRa-TWIN) Monitoring in Decommission-ing Environments

Supervisor: Dr Fatma Benkhelifa - f.benkhelifa@qmul.ac.uk

Application deadline: December 2024

Stipend: £21,237 per annum 

Indoor remote wireless sensing provides a paradigm shift in monitoring and managing nuclear assets more efficiently and effectively offering the possibility to collect real-time information with minimal hu-man interventions in hazardous environments. However, the use of batteries in indoor nuclear environ-ments can cause fire hazards and may require frequent battery replacement. This project will examine the use of energy harvesting to extend the operational lifetime of the sensors. Yet, radio frequency (RF) signals are put-upon to simultaneously harvest energy and transmit data for LoRa through-wall indoor nuclear (LoRa- TWIN) monitoring wireless sensor network. LoRa-TWIN includes an outdoor super LoRa node/gateway attached to the external wall of the indoor environment, and indoor LoRa nodes monitor-ing temperature, humidity, radiation, etc.

The project will design an RF energy harvesting circuit considering the system requirements of decom-missioning environments and optimize both the wireless through-wall power transfer and wireless through-wall information transfer by developing efficient and robust algorithms via machine learning techniques. Computer-based simulator and laboratory experiments will be exploited to assess the pro-posed algorithms and provide insightful guidelines for on-site implementation. The project outcomes will lead to research excellence in wireless powered remote monitoring without intrusive access and significant impact on monitoring procedures in decommissioning activities.

For more information about the project, please contact Fatma Benkhelifa - f.benkhelifa@qmul.ac.uk

Studentship

The PhD student will be supported by an industrial funded PhD bursary by Nuclear Decommissioning Agency (NDA). The student will receive supervision from QMUL as well as from National Nuclear Labor-atory (Dr Antonio Di Bueno, (NNL Technical Lead – Nuclear Instruments in the Instrumentation and In-situ Analysis Team). The student will have the opportunity to receive secondments at NNL as well as the industrial collaborators (NNL, Nuclear Restoration Services, Sellafield Ltd, Nuclear Waste Services).

The student will receive tuition fees and a London stipend at UKRI rates (a tax-free stipend of £21,237 per annum for the duration of the studentship) annually during the PhD period, which can span for 3.5 years.

How to apply

Queen Mary is interested in developing the next generation of outstanding researchers and decided to invest in specific research areas. Applicants should work with their prospective supervisor and submit their application following the School's application instructions.

The application should include the following:

· CV (max 2 pages)

· Cover letter (max 4,500 characters) stating clearly in the first page whether you are eligible for a scholarship as a UK resident (https://epsrc.ukri.org/skills/students/guidance-on-epsrc-stu-dentships/eligibility)

· Research proposal (max 500 words)

· 2 References

· Certificate of English Language (for students whose first language is not English)

· Other Certificates

Please note that both home students and international students qualify for this scholarship. For more information please see the EPSRC guidance. 

Application Deadline

The deadline for applications is December 2024.

For general enquiries contact Mrs Melissa Yeo at m.yeo@qmul.ac.uk (administrative enquiries) or Dr Ar-kaitz Zubiaga at a.zubiaga@qmul.ac.uk (academic enquiries) with the subject “EECS 2024 PhD scholarships enquiry”. For specific enquiries contact Dr Fatma Benkhelifa at f.benkhelifa@qmul.ac.uk

Title: Explainable AI to ensure trust in clinical Decision Support Systems (ExAIDSS) 

Supervisor: Dr Evangelia Kyrimi 

Application Deadline: 22nd November 2024

Project Description  

Technological breakthroughs have led to the development of sophisticated healthcare systems, but these will only become widely adopted if patients and healthcare professionals have confidence in their recommendations. Without a solution to the problem of user trust and user acceptance of healthcare technologies generally, the undeniable benefits of these systems will never be realised and all our efforts to develop accurate health-AI will be in vain. The ‘right to explanation’ and regulations on algorithmic decision-making already exist. Therefore, the ExAIDSS project focuses on translating causal AI models into explainable AI systems that users can trust and adopt in healthcare. The objectives of the project are: 

1. Investigate the fundamentals of explanation: Explore fundamental questions that have been neglected, such as what makes an explanation of AI “good”. 

1. Develop explanation algorithms that incorporate causality: Develop explanation algorithms that produces meaningful causal explanations for various types of reasoning. 

1. Create user-specific explanation outputs: Design an explanation that recognises who is interacting with it and the dynamics of clinical decision making. 

1. Create an evaluation protocol: Propose a protocol for evaluating different explanations purposes. 

1. Integrate the explanation algorithm and representation into existing healthcare digital platforms. 

The successful candidate will work on some of the above objectives. The project may build on prior research by the supervision team on topics such as AI adoption in healthcare and explainable AI. More details can be found in https://exaidss.com/ . There are also many opportunities for new directions.  The project may focus on theoretical or practical aspects, or a combination thereof. 

The PhD student will be supported by a QM Principal Studentship.  They will receive tuition fees and a London stipend at UKRI rates (currently in 2024/25 of £21,237 per year, to be confirmed for 2025/26) annually during the PhD period, which can span for 3 years. 

For more information about the project, please contact Evangelia Kyrimi (e.kyrimi@qmul.ac.uk). 

 Supervisor 

Dr Evangelia Kyrimi – e.kyrimi@qmul.ac.uk 
https://www.qmul.ac.uk/eecs/people/profiles/kyrimievangelia.html 
Google Scholar:  https://scholar.google.com/citations?user=ApdIq1YAAAAJ&hl=en 

How to apply  

Queen Mary is interested in developing the next generation of outstanding researchers and decided to invest in specific research areas.  For further information about potential PhD projects and supervisors please see the list of the projects at the end of this page. 

Applicants should work with their prospective supervisor and submit their application following the instructions at: http://eecs.qmul.ac.uk/phd/how-to-apply/    

The application should include the following:  

• CV (max 2 pages)   

• Cover letter (max 4,500 characters) stating clearly in the first page whether you are eligible for a scholarship as a UK resident (https://epsrc.ukri.org/skills/students/guidance-on-epsrc-studentships/eligibility)    

• Research proposal (max 500 words)  

• 2 References   

• Certificate of English Language (for students whose first language is not English)   

• Other Certificates   

Please note that to qualify as a home student for the purpose of the scholarships, a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship.  For more information please see: (https://epsrc.ukri.org/skills/students/guidance-on-epsrc-studentships/eligibility)   

Application Deadline  

The deadline for applications is the 22nd November 2024.  

For general enquiries contact Mrs Melissa Yeo at m.yeo@qmul.ac.uk (administrative enquiries) or Dr Arkaitz Zubiaga at a.zubiaga@qmul.ac.uk (academic enquiries) with the subject “EECS 2025 PhD scholarships enquiry”.  

For specific enquiries contact Dr Evangelia Kyrimi at e.kyrimi@qmul.ac.uk 

Primary supervisor: Dr Jin Zhang

Second supervisor: Professor Xiaodong Chen

Project Description

The extensive global consumption of fossil fuels has created severe climate change challenges, driving an urgent need for clean and sustainable energy sources. Nuclear fusion, often regarded as the ultimate energy solution, holds tremendous promise due to abundant fuel supply, high energy generation capacity, and clean operation free of greenhouse gas emissions. To enable nuclear fusion, fuel plasma needs to be heated to 100 million degrees, which requires high-power millimetre-wave sources. Using magnetrons as microwave sources offers numerous advantages, including compactness, high efficiency, simple assembly, and low cost. Therefore, developing suitable magnetrons could significantly accelerate realisation and commercial scaling of fusion power. This project aims to develop compact and cost-effective high-power magnetrons suitable for plasma heating in nuclear fusion facilities, utilising a novel magnetron design with extended interaction space for enhanced power.

Key objectives

(1) Develop a compact, low-cost, high-power, high-efficiency magnetron with novel extended interaction space.

(2) Test the magnetrons in plasma heating applications to validate their capabilities in nuclear fusion power generation.

(3) Advance commercialisation of fusion power by overcoming engineering and economic obstacles. For more information about the project, please contact Dr Jin Zhang (jin.zhang@qmul.ac.uk).

For more information about the project, please contact Dr Jin Zhang (jin.zhang@qmul.ac.uk).

Studentship

The PhD student will be supported by a QM Principal Studentship. They will receive tuition fees and a London stipend at UKRI rates (currently in 2024/25 of £21,237 per year, to be confirmed for 2025/26) annually during the PhD period, which can span for 3 years.

How to apply

Queen Mary is interested in developing the next generation of outstanding researchers and decided to invest in specific research areas. For further information about potential PhD projects and supervisors please see the list of the projects at the end of this page.

Applicants should work with their prospective supervisor and submit their application following the instructions at: http://eecs.qmul.ac.uk/phd/how-to-apply/

The application should include the following:

• CV (max 2 pages)

• Cover letter (max 4,500 characters) stating clearly in the first page whether you are eligible for a scholarship as a UK resident (https://epsrc.ukri.org/skills/students/guidance-on-epsrc-studentships/eligibility)

• Research proposal (max 500 words)

• 2 References

• Certificate of English Language (for students whose first language is not English)

• Other Certificates

Please note that to qualify as a home student for the purpose of the scholarships, a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship. For more information please see: (https://epsrc.ukri.org/skills/students/guidance-on-epsrc-studentships/eligibility)

Application Deadline

The deadline for applications is the 10 December 2024.

For general enquiries contact Mrs Melissa Yeo (administrative enquiries) or Dr Arkaitz Zubiaga (academic enquiries) with the subject “EECS 2025 PhD scholarships enquiry”. For specific enquiries contact Dr Jin Zhang.