Computer-aided methods for predictability of probabilistic discrete-time models with uncertainty

A fully funded 3-year PhD position is available at the Computer Science Department of the University of Liverpool. The Department is the world-leading research centre in Computer Science with a particular strength in theoretical computer science and artificial intelligence. In particular it offers a strong research environment spanning a wide range of research topics such as complexity theory and algorithms, distributed computing and computer networks, verification and formal methods, automata and computability theory, computational game theory, data mining, machine learning and natural language processing.

The successful candidate will do research in the areas of verification, theory of computation and algorithms designing new methods for predictability of probabilistic discrete-time models. The candidate will be supervised by Dr. Igor Potapov ( https://www.csc.liv.ac.uk/~igor/ ) and will become a member of Automata, Computability and Complexity Theory Group at the Department of Computer Science https://intranet.csc.liv.ac.uk/research/complexity/people.html

This PhD project will focus on theoretic limits, algorithm design and implementation of new verification procedures. We are interested to develop and apply new methods in the area of infectious disease control, where the model of disease spreading can be seen as a multilayer probabilistic system with complex dynamics and significant level of uncertainty. In particular, we plan to improve predictability of the disease spreading events by replacing currently used brute-force simulation modelling by more sophisticated state of art computer science methods. For example, we aim to reduce the level of uncertainty by identifying/computing unknown facts (e.g. more accurate probability ranges of events or identifying events with zero probability) and to develop new control measures by applying game theoretic approaches for infectious disease control problems. As an initial step we can apply iterative abstraction refinement using existing model checking systems such as PRISM (a tool for formal modelling and analysis random/probabilistic systems) and later investigate a possibility of developing own methods to compute/evaluate/establish new constraints that currently are not taking into account in explicit brute-force simulation modelling.

The position will be based in University of Liverpool’s Department of Computer Science and supervised by Dr. Igor Potapov. In addition due to interdisciplinary nature of the proposal the position will be co-supervised by Dr. Kieran Sharkey (Mathematical Sciences) and Prof. Matthew Baylis (Epidemiology and Population Health).

Prior Experience:
We welcome talented and highly motivated candidates with good first degree (BSc or MSc) in Computer Science, Mathematics or closely related subject. A programming experience and/or previous research experience would be a distinct advantage though it not essential. The applicant must have good communication skills, both verbal and in writing (English), be self-motivated and helpful team member.

How to Apply:
Applications should be made formally by following the University of Liverpool’s standard process. Details can be found here: https://www.liverpool.ac.uk/computer-science/postgraduate/phdstudy/applications/ Applications should list Dr Igor Potapov as the potential supervisor and choose the option "School funded PhD" when asked how you will fund the PhD. Applications must contain a cover letter, a curriculum vitae or resume, copies of undergraduate and graduate transcripts, a 1-2 page research statement describing how the applicant’s qualifications and research interests would fit the project, a copy of the applicant’s bachelor or master’s thesis and the names and contact information of academic references

Applications are admitted until 20 November 2017, or until a suitable candidate is found thereafter. The selected candidate can start working immediately, but the latest starting date for this fully-funded PhD position is 1 March 2018.