Research Studentship in Generative AI for Renewable Development

4-year D.Phil. studentship 

Project: Generative AI Co-Pilots for Renewable Developers

Supervisors: Professor Thomas Morstyn and Dr Frank Tutu

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. To address this, we will leverage the new opportunity created by interactive AI co-pilots that can assist human experts with analysis and decision-making. The studentship will involve close collaboration with the EDF R&D UK Centre, which is supporting the project through an EPRC Industrial Doctoral Landscape Award. 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) identifying critical information from documentation (e.g. site surveys, contracts, market rules); (2) generating evidence-backed responses to analyst queries by calling upon specialised software tools (e.g. for modelling, forecasting, optimisation); and (3) proactively suggesting alternative design options.

Q2: To what extent can the AI co-pilots improve 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 home students, but there might be some flexibility to support an overseas candidate (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 2024/24.

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). The main academic supervisor is Professor Thomas Morstyn, and the industry supervisor is Dr Frank Tutu, Lead Research Engineering at EDF R&D 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. Details are available on the course page of the University website.

Please quote 25ENGEL_TM in all correspondence and in your graduate application.

Application deadline: noon on 4th March 2025 (In line with the University admissions deadline set by the University)

Start date: October 2025

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