Research Fellow position starting 1 Sep ‘20. Funded by the Leverhulme Trust: Bayesian Inference and Approximations of High-Dimensional Network Models.
We seek to hire a highly motivated and talented Postdoctoral Research Fellow on a fixed term 16-months position starting 1st of September 2020. This is a highly desirable position funded by the Leverhulme Trust research project grant (RPG-2017-370: Bayesian Inference and Approximations of High-Dimensional Network Models).
The use of networks to model complex systems has had a significant impact on the way in which brains, epidemics and social interactions are modelled. However, many of the resulting mathematical models suffer from high model dimensionality and therefore limited analytical tractability, sensitivity to incomplete information about the network, and inaccuracies due to simplifying assumptions or approximations.
This research will seek to harness techniques from stochastic analysis, partial differential equations (PDEs) and uncertainty quantification into a new framework for the analysis and inference of network-based models. A secondary aim is the development of rigorous numerical methods that may also prove crucial to gain intuition and guide development of models and analysis.
Some of the results from the first part of the project can be found at (i) https://arxiv.org/abs/1906.10966 and (ii) https://arxiv.org/abs/2004.04636 .The Research Fellow will be expected to play a key role in continuing these research directions and strongly encouraged to contribute their own.
The post is part of a larger project funded by the Leverhulme Trust which involves a PhD Student and a Teaching Fellow. The post-holder will work closely with the principal investigator Prof Istvan Kiss (http://users.sussex.ac.uk/~izk20/) and co-investigators Dr Masoumeh Dashti and Prof Luc Berthouze at University of Sussex. The project includes a research visit to co-investigator Prof Andrew Stuart at Caltech.
The fellowship provides full support that includes salary (this will be set according to skills and abilities of successful candidate) and support for research activities. The successful candidate will benefit from interactions with leading applied mathematicians within the Department of Mathematics and research project collaborators in the UK and USA. The candidate will pursue independent research in a fast-emerging field and will acquire advanced mathematical and computational techniques in areas such as Inverse Problems, Bayesian Inference, Uncertainty Quantification and numerical methods for stochastic processes on networks.
Prospective candidates should hold a PhD in Mathematics/Statistics/Physics, and have a strong background in either Bayesian Inference, Inverse Problems and Partial/Stochastic Differential Equations or Uncertainty Quantification with expertise in complex systems being a bonus.
The University is committed to equality and valuing diversity, and applications are particularly welcomed from women and black and minority ethnic candidates, who are under-represented in academic posts in Science, Technology, Engineering, Medicine and Mathematics (STEMM) at Sussex.
Download job description and person specification Ref 3669 [PDF 205.00KB]
- University of Sussex
- United Kingdom
- Closing date
- June 1st, 2020
- Posted on
- May 1st, 2020 23:21
- Last updated
- May 1st, 2020 23:21