Funding

Self-funded

Project code

CMP10510529

Department

School of Computing, Mathematics and Physics

Start dates

October, February and April

Application deadline

Applications accepted all year round

Applications are invited for a self-funded, 3 year full-time or 6-year part-time PhD project.  

The PhD will be based in the School of Computing, Mathematics and Physics and will be supervised by Dr Fahad Ahmad. 

 

The work on this project will:

  • Develop quantum machine learning models to accelerate the discovery of novel materials for energy storage applications
  • Integrate and analyse large-scale open materials databases (such as Materials Project, OQMD, and others) using advanced data-driven approaches
  • Apply hybrid quantum-classical algorithms to improve prediction accuracy of material properties such as conductivity, stability, and energy density
  • Design scalable computational pipelines combining quantum computing based simulations with machine learning techniques
  • Explore explainable AI approaches to understand the underlying physics governing material performance

The growing global demand for sustainable and efficient energy storage solutions has intensified the need for rapid discovery and optimisation of advanced materials. Traditional experimental and computational approaches for materials discovery are often time-consuming and computationally expensive. This PhD project aims to address these challenges by leveraging the emerging field of quantum machine learning (QML) in combination with open materials databases to accelerate the identification and design of next-generation energy storage materials.

The project will focus on integrating large-scale datasets from publicly available materials repositories with advanced machine learning techniques and quantum computing methods. By utilising hybrid quantum-classical algorithms, the research will explore new ways to model complex material behaviours, enabling faster and more accurate prediction of key properties such as electrochemical performance, thermal stability, and efficiency.

A key component of the project involves developing robust data pipelines that combine quantum computing based simulations with machine learning models. Additionally, explainable AI techniques will be incorporated to provide insights into model predictions, helping to uncover the fundamental scientific principles driving material performance. This interpretability will be crucial for guiding experimental validation and fostering trust in AI-driven discoveries.

This PhD offers a unique opportunity to work at the intersection of quantum computing, machine learning, and materials science. Students will gain expertise in cutting-edge computational techniques, quantum algorithms, and large-scale data analysis. They will also develop strong interdisciplinary research skills, enabling collaboration across physics, chemistry, and computing domains.

After completion, graduates will be highly competitive for careers in academia, research institutions, and industries such as energy, AI, and advanced materials, with opportunities in roles like data scientist, quantum computing researcher, and AI specialist.

 

 

Fees and funding

Visit the research subject area page for fees and funding information for this project.

Funding Availability: Self-funded PhD students only.

PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only - eligibility criteria apply).

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable. 

Entry requirements

You’ll need a good first degree (minimum upper second class or equivalent) or a Master’s in Computer Science, Data Science, Physics, Materials Science, or a related area. Other degrees with a strong computational or analytical component, and candidates with relevant industry experience, will also be considered.

English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

International students will require a study visa from UKVI to pursue the degree in the UK. If the research is in a sensitive or technological subject, the student may also need to secure an Academic Technology Approval Scheme (ATAS) certificate from the UK Foreign Office.

Strong foundation in programming (e.g., Python), understanding of machine learning algorithms, familiarity with linear algebra and quantum computing concepts (or willingness to learn), and an interest in materials science and energy applications. Experience with data analysis, simulation tools, or scientific computing will be advantageous.

 

How to apply

We’d encourage you to contact Dr Fahad Ahmed ([email protected]) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake.. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.  Our ‘How to Apply’ page offers further guidance on the PhD application process.

When applying please quote project code CMP10510529.