Principled agent-based simulation
Agent-based simulation can be used to model the dynamics of natural systems: each agent represents a low-level component (e.g. a cell), and the finished simulator aims to model the higher level dynamics (e.g. the progress of a disease or development of part of an embryo). A well-engineered and documented simulation can be used to address questions and run experiments that cannot be run in vivo, for instance because the experimental set-up perturbs the dynamics of the system studied.
I have been involved with simulation projects over a decade, and have access to a range of example projects. However, to date, every simulation has been designed and implemented afresh. This PhD is to explore software engineering support for simulation, with the aim of producing design and implementation support that enables reuse and modification of simulators.
Target design languages are behavioural models (e.g. any form of state machine, Petri nets, UML activity and sequence diagrams). Target implementation languages are currently Java Mason (several existing simulators) or FlameGPU (potential collaboration to develop new simulators).
We anticipate using model-driven engineering and model transformation to achieve design-to-code mappings and support reuse and re-engineering.
The PhD would be based at Keele in Prof. Polack’s team, but would involve working with developers at University of York, and the York Computational Immunology Lab.
The research will be supervised by Prof. Fiona Polack in the Centre for Computer Science Research at Keele University
Open to fully self-funded students only.
Please note that self-funded applicants must provide funding for both tuition fees and living expenses for the 3 year duration of the research. There is a future possibility of competitive scholarship awards for outstanding applicants (1st class honours), however, none are currently available.
For information regarding University tuition fees please see http://www.keele.ac.uk/pgresearch/feesandfinance/
Please quote FNS GS 2018-08 on your application.
No funding available.
Opportunity for self-funded applications only.
Eligibility Criteria: Applications are welcomed from science, technology, engineering or mathematics graduates with (or anticipating) at least a 2.1 honours degree or equivalent. Applicants will require good computing skills, ideally with good working knowledge of modelling (UML, Petri nets) and implementation using model transformation. They should be self-motivated and have the ability to work both independently and as part of a team.
The opportunities are open to UK/EU students and overseas students. The collaborative and presentation aspects of the research require good English language and communication skills.