The University of Bath is advertising a competitive PhD on causal artificial intelligence for resilient low-carbon energy systems. International applicants are eligible for consideration, subject to an overseas studentship cap. Applications close on 29 November 2026, with an expected start on 4 October 2027. The opportunity may interest applicants who want to connect AI methods with careful reasoning about engineering decisions.
Research Focus
The project combines knowledge graphs, causal inference and a causal digital twin. It considers cascading failures and counterfactual interventions in energy systems. AI agents are another proposed research direction. When evaluating your interest, consider whether you enjoy asking why a result occurs and what evidence would be necessary before treating a proposed intervention as useful.
Location, University and Academic Contact
The university is in Bath, United Kingdom. The research concerns low-carbon electricity-system resilience. Electronic and Electrical Engineering hosts the opportunity. The faculty’s studentship competition provides the funding route. Dr Chenghong Gu is the named contact. Informal enquiries are encouraged.
Eligibility and Funding
A first or 2:1 honours degree or international equivalent is required, with a master’s advantageous. Non-UK applicants not currently studying in the UK must satisfy the English requirement before the deadline. Competitive awards cover tuition, £1,000 annual training support and a stipend listed at £21,805 for 2026/27. Overseas places are capped, visa and Immigration Health Surcharge costs remain personal expenses, and the published rate does not confirm the stipend for 2027/28.
Thinking About Causal Questions
For optional preparation, imagine that two measurements change together in a small analytical example. Write down several possible explanations for that relationship before deciding that one measurement must cause the other. Consider whether an unobserved influence could affect both, and identify information that would help distinguish the explanations. This exercise is general research preparation rather than an advertised assessment task or a claim about the data available in the doctoral project.
You could then describe an intervention in your example and ask what prediction would follow from each explanation. Make clear which assumptions support the prediction and what observation could show that an assumption was wrong. Distinguish a plausible story from evidence that a proposed change would produce the intended effect. A concise explanation of these limits can demonstrate thoughtful preparation without requiring you to claim that you have already solved the project’s central research problem.
Explaining Your Technical Contribution
If the application asks for supporting materials, use a previous project to show how you connect a computational method with the question it is meant to answer. Explain what you implemented, what information it used and how you judged its performance. Be explicit about which parts you developed yourself and which parts came from an existing tool or a collaborator. Avoid treating a successful software demonstration as proof that every underlying scientific assumption has been validated.
If you have worked on a predictive model, describe what its evaluation established and what it left unresolved. You might have checked a held-out sample, compared a baseline or investigated a failure case, but mention those activities only if you actually completed them. Explain how the experience shaped the questions you would now ask about a new research problem. This approach gives the reader concrete evidence of your judgement while keeping your account proportionate to your actual experience.
Choosing a Focused Motivation
A useful motivation statement should explain what attracts you to the advertised problem rather than praising artificial intelligence in general. Identify a particular question you want to understand and connect it to a course, dissertation, placement or independent study that genuinely influenced you. Separate the knowledge you already have from the expertise you hope to gain through doctoral research. Do not present optional preparation suggestions as programme requirements, and follow the actual form’s instructions about the length and content of any statement.
How to Apply
Use the university’s current online instructions to confirm the complete submission checklist. Check that your project title, intended supervisor and programme selection are consistent across the form. A reply to an informal enquiry can help clarify research fit, but admission and studentship selection still require the formal process.
- View scholarship details and follow the institutional application route.
- Choose the Electronic and Electrical Engineering PhD and University of Bath URSA funding.
- Identify the exact project and intended supervisor; the competition permits a maximum of two project choices.
Before Submitting
Information was checked on 23 October 2026. Reconfirm the deadline, current admissions conditions and award terms before applying. Retain the submitted form and any written funding clarification, and review personal expenses separately before making commitments that depend on receiving an international award.