PEPSRC Doctoral Landscape Award: Here’s what matters. The University of Exeter is offering a fully funded PhD position under the Engineering and Physical Sciences Research Council Doctoral Landscape Award. The project explores how evolutionary algorithms and reinforcement learning can help autonomous systems learn accurate, real-time world models in unpredictable environments such as crowd management, wireless sensor networks, healthcare, and environmental monitoring. The work includes collaboration with INOCESS Inc., France, providing real data, industrial supervision, and possible field testing.
PhD Scholarship: Evolving World Models for Self-Adaptive and Autonomous Systems (EPSRC Doctoral Landscape Award)
Summary Table
| Field | Details |
|---|---|
| Designation | PhD Researcher |
| Project Title | Evolving World Models using Evolutionary Reinforcement Learning |
| Supervisor | Dr Huma Samin |
| Co-supervisors | Dr Aishwaryaprajna, Prof Khurram Bhatti |
| Department | Computer Science |
| Programme | PhD in Computer Science |
| Location | Kathleen Booth Building, Streatham Campus, University of Exeter |
| Funding | EPSRC Doctoral Landscape Award |
| Start | Not specified (standard EPSRC cycle) |
| Industrial Partner | INOCESS Inc., France |
Designation
PhD Researcher – Engineering and Physical Sciences Research Council (EPSRC) Doctoral Landscape Award.
Research Area
Self-adaptive and autonomous systems, evolutionary algorithms, reinforcement learning, world model learning, dynamic environments, decision-making under uncertainty.
Location
Kathleen Booth Building
Streatham Campus
University of Exeter, UK
Eligibility / Qualification
Bottom line: you need a strong background in computing and interest in AI.
Required
- Bachelor’s degree in Computer Science or a related field (2:1 or above).
Preferred
- Experience with reinforcement learning or optimization.
- Research exposure (not mandatory but helpful).
Job Description
You’ll investigate how autonomous systems can learn and update their understanding of the world as conditions shift. The project focuses on:
- Building and updating probabilistic world models using reinforcement learning.
- Developing hybrid evolutionary-RL frameworks that refine model structures.
- Improving decision-making in uncertain environments.
- Testing models in realistic scenarios like crowd management and communication networks.
You’ll also gain hands-on training with INOCESS Inc., including access to industry data, co-supervision, and potential field deployment opportunities.
How to Apply
Use the official application link provided by the University of Exeter: APPLY HERE
For project-specific questions, contact Dr Huma Samin (h.samin@exeter.ac.uk).
Make sure you review the PhD programme entry requirements before applying.
Last Date to Apply
Not mentioned in the provided text. Please check the official application portal for the deadline.
