Research Studentship in Generative AI Co-Pilots for Renewable Developers

4-year D.Phil. studentship 

Project: Generative AI Co-Pilots for Renewable Developers

Supervisor: Professor Thomas Morstyn and Dr Frank Tutu

The aim of this studentship is to investigate the design of generative AI co-pilots to deliver faster and cheaper renewable generation projects. Renewable project planning involves complex and time-consuming work by experts across resource modelling, engineering design, and techno-economic analysis. Design efficiencies are highly valuable given the UK is investing £10bn+ per year in renewable generation. Recently, there have been major advances in the use of generative AI models to create interactive AI co-pilots which can assist human experts with analysis and decision making.

The project will extend foundation models with specialised software toolkits and domain-specific knowledge using ReAct prompting, supervised fine-tuning and reinforcement learning. We will also explore state-of-the-art methods from explainable AI to establish trust in co-pilot outputs.

The studentship will involve close collaboration with energy researchers and renewable project designers from EDF, which is the largest generator of zero-carbon electricity in the UK. EDF is supporting the project through an Engineering and Physical Sciences Research Council (EPSRC) Industrial Doctoral Landscape Award.

Eligibility

This studentship is open to Home students.

Award Value

Course fees are covered at the level set for Home students c. £10,470 p.a. The stipend (tax-free maintenance grant) is the UKRI Minimum Stipend c. £20,780 p.a. for the first year, and at least this amount for a further three years.

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 Engineering, Computer Science or another relevant field
• Excellent English written and spoken communication skills
• Strong programming skills (preferably with Python)

The following skills 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 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. Details are available on this course page of the University website.

Please quote 26ENGEL_TM1 in all correspondence and in your graduate application.

Application deadline: noon on 3 March 2026 (In line with the University admissions deadline set by the University).

Start date: October 2026