Research Studentship in Engineering IDLA: Generative AI Co-Pilots for Renewable Developers

4-year DPhil studentship 

Supervisors: Professor Thomas Morstyn and Dr Frank Tutu (Lead Research Engineer at EDF R&D UK)

The aim of this studentship will be to investigate the opportunity for specialised generative AI co-pilots to help renewable developers take projects from conception to implementation. As part of the UK’s net-zero transition, £10bn+ is being invested per year in renewable generation. Project planning is critical for success, but it is also highly complex and requires significant time from expert analysts. Recent advances in generative AI models have led to interactive AI co-pilots that assist human experts in analysis and decision-making. In particular, the impressive natural language capabilities of large language models (LLMs) enable them to interact with users, gather citation-supported information, and utilise external software tools via API calls. Multi-modal LLMs can further integrate other data modalities (e.g. images, time series). These capabilities have the promising potential to support renewable project planning, facilitating decarbonisation in the UK and worldwide. The studentship will involve close collaboration with the EDF R&D UK Centre, which is supporting the project through an Engineering and Physical Sciences Research Council (EPSRC) Industrial Doctoral Landscape Award. The studentship will address three interconnected research questions:

The studentship will address three interconnected research questions:

Q1: How can we design specialised generative AI co-pilots to support the planning of renewable energy projects? Key capabilities will include (1) the ability to identify critical information from documentation (e.g. site surveys, contracts, market rules); (2) the ability to generate evidence-backed responses to analyst queries by calling upon specialised software tools (e.g. for renewable modelling, power flow analysis, financial forecasting); and (3) the ability to proactively suggest design alternatives which can mitigate risks and increase financial viability. .

Q2: To what extent can the AI co-pilots facilitate renewable planning? Answering this question will involve validating the benefits of the AI co-pilots in a production environment in collaboration with EDF.

Q3: How can we design AI co-pilots which are able to continually improve as new methods, data and computing resources become available?

Eligibility

The studentship is open to home and overseas students (full award – home fees plus stipend).

Award Value

Course fees are covered at the level set for Home students. The stipend (tax-free maintenance grant) is the UKRI minimum stipend (currently £19,237 for 2024/25).

Candidate Requirements

Prospective candidates will be judged according to how well they meet the following criteria:

• A first class or strong upper second-class undergraduate degree with honours (or equivalent) in Electrical Engineering, Computer Science or another relevant field.
• Excellent English written and spoken communication skills
• Strong programming skills (preferably with Python).

The following criteria are desirable but not essential:

• Experience with optimisation and/or machine learning
• Experience using large language models
• Experience with power system modelling
• Relevant industry and/or research experience.

Application Procedure

Informal enquiries are encouraged and should be addressed to Prof Thomas Morstyn (thomas.morstyn@eng.ox.ac.uk).

Candidates must submit a graduate application form and are expected to meet the graduate admissions criteria. With your application you should include a personal statement explaining how you satisfy each of the selection criteria. To apply please visit the course page of the University website.

Application deadline: noon on Friday 16 May 2025

Start date: October 2025