PhD Studentship: Continual Learning in Meta-heuristic Optimisation
|Published||March 21, 2023|
|Location||Edinburgh, United Kingdom|
Edinburgh Napier University is ranked the top modern University in Scotland in the 2022 Times World University Rankings. The School of Computing, Engineering and the Built Environment is highly regarded and has invested recently heavily in research in terms of both staff and facilities to conduct world class research in a wide range of disciplines.
In the 2021 Research Excellence Framework (REF), our research was ranked top modern university in Scotland in terms of research power.
As part of our recent significant investments in research, we have recently recruited additional academics with outstanding research capabilities.
The investment in research is continuing with a large number fully funded 3-year PhD studentships being made available of which this is one. The studentship will cover full UK or international tuition fees and will include a standard living allowance at the RCUK rate (Currently £17,668 pa).
At this stage, we are recruiting students for following projects:
- Generating diverse and functional robots by jointly optimising their body-plan and controllers
- Adaptive Robot Behaviours in dynamic and outdoor settings
- Continual Learning for Combinatorial Optimisation
A brief description of the projects is shown below. More information on requirements and how to apply is available following the provided links.
The studentship is expected to start in October 2023. All applications must be received by 14th April. Those who have not been contacted by 28th April 2023 should assume that they have been unsuccessful.
More information about PhD degrees at Napier can be found at https://www.napier.ac.uk/research-and-innovation/research-degrees.
Continual Learning in Meta-Heuristic Optimisation
Director of Studies: Professor Emma Hart (email@example.com)
Optimisation problems are ubiquitous across many sectors. In a typical scenario, instances arrive in a continual stream and a solution needs to be quickly produced. Meta-heuristic search techniques have proved useful in providing high-quality solutions, but rather than simply being designed and deployed in one-off process, they should (a) be capable of continually adapting to changing instances to ensure they deliver the best quality solutions and (2) improve over time, by learning from experience gleaned from solving previous solutions. This project will focus on one or more aspects of creating a continual learning system, for instance developing novel algorithm-selection methods that are capable of selecting the most appropriate method; using algorithm-generation methods (e.g. genetic programming) to generate or tune algorithms to work well on instances that occur in novel regions of the instance space; developing methods that are capable of learning from experience, for example using transfer learning or warm-starting methods using knowledge learned from solving past instances. The project is likely to mix techniques from meta-heuristic optimisation and machine-learning, particularly borrowing ideas from the transfer learning or continual learning literature.
For further details and how to apply please visit