PhD Studentship - Machine Learning-Guided Synthesis of Graphene Aerogels for Supercapacitors

Application deadline: All year round

How to apply: uom.link/pgr-apply

This 3.5-year PhD project is fully funded, and home students are eligible to apply. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£21,805 for 2026/27) and tuition fees will be paid. We expect the stipend to increase each year. The start date is October 2026.

We recommend that you apply early as the advert may be removed before the deadline.

Graphene aerogels are ultra-light and highly porous carbon materials with strong potential for high-power energy storage applications such as electric double-layer capacitors (EDLCs). However, their synthesis remains largely empirical, with limited predictive understanding of how processing parameters control pore structure and electrochemical performance.

This PhD project aims to develop a data-driven framework for graphene aerogel design by integrating structured experimental Design of Experiments (DoE) with machine learning (ML). The student will generate high-quality experimental datasets, establish process-structure-property relationships, and demonstrate ML-guided optimisation of aerogel electrodes. The outcomes are expected to effectively contribute to scalable, rational design strategies for next-generation porous carbon materials and high-performance energy storage devices.

Research Objectives and Methodology

The project will combine controlled synthesis of freeze-templated graphene aerogels with systematic variation of key processing parameters using a DoE approach. Structural, physical, and electrochemical properties will be characterised and used to train and validate ML models that link synthesis conditions to performance metrics, enabling predictive optimisation of aerogel-based supercapacitors.

The project will be undertaken under the supervision of Dr Mehrdad Vasheghani Farahani from Department of Chemical Engineering, with co-supervision by Professor Ian Kinloch from Department of Materials. The successful candidate will develop strong expertise in porous materials synthesis, advanced electrochemical characterisation, and machine learning for materials science. They will be part of large collaborative research community that spans the Faculty, Henry Royce Institute, and the National Graphene Institute.

Applicants should hold (or be about to obtain) a First or Upper Second class (2:1) UK honours degree (or international equivalent) in a relevant subject such as Chemical Engineering, Materials Science, Physics, Chemistry, or a closely related discipline.

Applications are invited for a fully funded 3.5-year studentship covering stipend and fees. The PhD is expected to start in September 2026. Applications will be considered until the position is filled, and early application is encouraged as the advert will be withdrawn once the post has been filled.

This studentship is available to Home students only.

To apply, please contact the main supervisor; Dr Mehrdad vasheghani Farahan - mehrdad.vasheghanifarahani@manchester.ac.uk. Please include details of your current level of study, academic background, and any relevant experience, together with a cover letter explaining your motivation to undertake this PhD project. It is essential that you also include a copy of your CV and transcripts. Please include the project title and your name in the subject line of your email.

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