Don't miss our weekly PhD newsletter | Sign up now Don't miss our weekly PhD newsletter | Sign up now

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  Prof Emma Hart  No more applications being accepted

About the Project

This PhD project offers opportunities to explore optimisation, learning and/or adaptation in the context of evolutionary robotics. Possible avenues of research include the co-evolution of morphology and control of robots; the interaction of evolution and learning mechanisms to produce bodies and behaviours that are specialised to specific environments and tasks. Alternative projects could focus on adaptation of behaviour only, using learning methods (e.g. evolution, reinforcement learning) to adapt controllers in real time to adapt to new environments, or learning repertoires of behaviours to enable robust performance.

Another promising area is in the use of state-of-the art methods from the quality-diversity literature to fully explore rich search spaces of both morphologies and controller. Projects can be conducted in simulation only but there is also the possibility to utilise our robotics laboratory to conduct experiments on physical robots.

Academic qualifications

A first degree (at least a 2.1) ideally in Computer Science, however a degree in another scientific subject with a good fundamental knowledge of computer science is also acceptable.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes

  • Experience of fundamental concepts in Evolutionary Computation or other stochastic search optimisation methods
  • Competent in programming (e.g. in Python/C++), good knowledge of statistics and data-analysis
  • Knowledge of machine-learning methods (particularly for Project 2)
  • Good written and oral communication skills
  • Strong motivation, with evidence of independent research skills relevant to the project
  • Good time management

For enquiries about the content of the project, please email Professor Emma Hart [Email Address Removed] For information about how to apply, please visit our website To apply, please select the link for the PhD Computing FT application form

Computer Science (8) Engineering (12) Mathematics (25)

Funding Notes

Funded PhD Project (Worldwide)


Gupta, Agrim, et al. "Embodied intelligence via learning and evolution." Nature communications 12.1 (2021): 1-12.
Le Goff, Léni K., et al. "Morpho-evolution with learning using a controller archive as an inheritance mechanism." IEEE Transactions on Cognitive and Developmental Systems (2022).
Hart, Emma, and Léni K. Le Goff. "Artificial evolution of robot bodies and control: on the interaction between evolution, learning and culture." Philosophical Transactions of the Royal Society B 377.1843 (2022)
Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.