IDLA studentship: Generative AI co-pilots for renewable developers
The aim of this fully-funded research project is be to investigate the opportunity for specialised generative AI co-pilots to help renewable developers take projects from conception to implementation.
Key facts
Application deadline
12:00 midday UK time on Tuesday 3rd March 2026
Applications may remain open after this deadline if places are still available.
Places available
1
Academic supervisor
Professor Thomas Morstyn
Funding (fully-funded scholarship)
This project will be offered as a fully-funded scholarship, which will include:
- all course fees for the duration of your course; and
- a living stipend.
This project is funded by an Engineering and Physical Sciences Research Council (EPSRC) Industrial Doctoral Landscape Award (IDLA).
Due to restrictions on international student recruitment to UK Research and Innovation (UKRI) grants, only applications from applicants who meet the residential eligibility criteria for UKRI funding will be considered.
The industrial partner for this project is EDF R&D UK.
Expected start date
Expected duration
Full time: 3-4 years
Part time: 6-8 years
About this project
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:
- How can we design specialised generative AI co-pilots to support the planning of renewable energy projects? Key capabilities will include:
- identifying critical information from documentation (eg site surveys, contracts, market rules);
- generating evidence-backed responses to analyst queries by calling upon specialised software tools (eg for modelling, forecasting, optimisation); and
- proactively suggesting alternative design options.
- 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.
- How can we design AI co-pilots which are able to continually improve as new methods, data and computing resources become available?
Course details
Unless stated otherwise, this project information is subject to the more detailed information provided on the page of the offering course.
Course offering this project
You should familiarise yourself with the details of this course before applying for this project.
Entry requirements
This studentship project is funded by UKRI. Due to restrictions on international student recruitment to UKRI grants, only applications from applicants who meet the residential eligibility criteria for UKRI funding will be considered.
In addition to the entry requirements for the main course, prospective candidates for this project 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; and
- 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; and
- relevant industry and/or research experience.
Please refer to the DPhil in Engineering Science course page for all other entry requirements, including the required level of English language proficiency.
College preference
If you have the option of stating a college preference for this project, the available colleges will be confirmed within the application form.
For some projects, including all of those offered after the standard course has closed to applications, the department will assign your application to a college.
How to apply
The guidance for this project may differ from the standard course - please read it carefully.
Application fee waivers (for all applications via this page)
Applications to this project should be made only via this page and not the related course page.
The application fee will be waived for all applications made via this page.
Contacting the department before making an application
It is recommend that you contact the project supervisor, Professor Thomas Morstyn ([email protected]), ahead of submitting your application.
Guidance for completing the application form
After you have started an application via this page, please refer to the application instructions on the main course page.