Funded Studentships

You can use our search listing to find the right project for your skills, passion and expertise. The listing includes:

  • An extensive portfolio of PhD projects, with expert supervisory teams based in one of our leading research centres.
  • A number of PhD programmes with international partner universities around the world.

Please note: projects are updated regularly.

You may also be interested in our PhD by Publication.

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Trailblazers: The Early Career Researcher and PhD Candidate Partnering Scheme

The scheme provides PhD candidates with an innovative and dynamic intellectual space in which to undertake transformative research, whilst fully supported by a team of experienced supervisors.

Explainable Neuro-Symbolic AI for Novel Mathematical Sciences Pedagogy

Dual degree project between Coventry University (UK) and Stellenbosch University (South Africa) exploring new methodologies in explainable neuro-symbolic artificial intelligence applied to overcome data scarcity barriers in AI for mathematics education.

Microplastics and chemical additives from virgin and recycled Polyethylene terephthalate (PET) packaging material: Leachability, toxicity, and behavioural aspects

This interdisciplinary project investigates the release of microplastics (MPs) and chemical additives from virgin polyethylene terephthalate (vPET) and recycled polyethylene terephthalate (rPET) bottled water, and assesses their potential impacts on human health and consumer perception.

Nanowire synthesis enabled by novel magnetic catalysts and electrochemical deposition

This fully funded project aims to develop metallic nanowires using copper and nickel electroless deposition initiated by magnetic particles aligned in a magnetic field.

Accelerating the PEM fuel cell development cycle by predicting large-format stack performance and durability from subscale membrane electrode assembly testing

This fully-funded PhD studentship aims to develop and validate a scale-transfer methodology that predicts large-format PEMFC stack performance and durability from subscale cell tests, identifying which electrochemical diagnostics, operating regimes and degradation indicators remain transferable across active area, flow-field architecture and hardware scale.

Gen AI and Student Feedback Literacy Coventry led Studentship

This project examines how university students’ feedback literacy is transforming amid widespread GenAI adoption, which is a critical challenge for educators and policymakers globally. Focusing on learner agency, the successful candidate will explore whether feedback opportunities effectively engage students, despite persistent disengagement. Project outcomes will inform curricular design, institutional support strategies, and pedagogical practices, to bridge gaps between theory and real-world engagement. Candidates from a range of disciplinary backgrounds are welcome to apply. Experience of social science research approaches is needed.

AI-Enhanced EMC Digital Twin for Automotive WBG Converters

This PhD studentship develops an AI-driven Digital Twin framework for Electromagnetic Compatibility (EMC) in Wide-Bandgap (SiC/GaN) power converters used in automotive applications. The project will develop predictive surrogate models using Physics-Informed Neural Networks (PINNs) and Gaussian Process Regression (GPR) to predict high-frequency conducted and radiated emissions, bridging virtual simulations with real-world physical compliance testing.

Gen AI and Student Feedback Literacy Deakin Led Studentship

This project examines how university students’ feedback literacy is transforming amid widespread GenAI adoption, which is a critical challenge for educators and policymakers globally. Focusing on learner agency, the successful candidate will explore whether feedback opportunities effectively engage students, despite persistent disengagement. Project outcomes will inform curricular design, institutional support strategies, and pedagogical practices, to bridge gaps between theory and real-world engagement. Candidates from a range of disciplinary backgrounds are welcome to apply. Experience of social science research approaches is needed.