Research Fellow in Mathematics Ref 3077

School/department: School of Mathematical and Physical Sciences
Hours: Full time
Contract: Fixed term for three years (01 September 2018 to 31 August 2021)
Reference: 3077 
Salary: Starting at £32,548 and rising to £38,833 per annum
Placed on: 11 April 2018
Closing date: 31 May 2018. Applications must be received by midnight of the closing date.
Expected interview date: 14 June 2018
Expected start date: 01 September 2018

Job description

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 3-year full-time position starting 1st of September 2018.

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 revolutionised the way in which brains, epidemics, social interactions and more generally the flow of information 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.

The aim of this research is to develop a new modelling paradigm that will tackle these challenges as well as offer several other major benefits. This paradigm relies on the specification of a new class of parametric models that are flexible enough to handle networks currently out of reach of state-of-the-art models.

The inference of the parameters is formulated as Bayesian inverse problems, which in turn makes it possible to rigorously quantify the uncertainty introduced by simplifying assumptions and incomplete network data.

This research will seek to harness a novel combination of techniques from stochastic analysis, partial differential equations (PDEs) and uncertainty quantification and could prove a step change in the ability of network science to deal with real-world applications.

The Research Fellow will play a key role in the formulation and numerical solution of Bayesian inverse problems in the context of stochastic epidemics on networks.

This will ultimately be used to infer networks and their structure from simulated or real outbreak data.
Furthermore, the post-holder will contribute to the development of rigorous uncertainty quantification for the estimated parameters, as well as for quantifying the uncertainty induced by simplifying assumptions such as closures.

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 and co-investigators Dr Masoumeh Dashti and Dr Luc Berthouze at University of Sussex and 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.

Enquiries: All enquiries should be directed to Prof Istvan Kiss ( and Dr Masoumeh Dashti (

The School of Mathematical and Physical Sciences is committed to equality and valuing diversity, and currently holds an Athena SWAN Bronze Award.

Download job description and person specification Ref 3077 [PDF 195.71KB]

How to apply

Download our academic post application form [DOC 301.50KB] and personal details and equal opportunities form [DOC 162.50KB] and fill in all sections.

Prospective candidates should hold a PhD in Mathematics/Statistics/Physics, and have a strong background in either Bayesian Inference, Inverse Problems or Uncertainty Quantification with expertise in complex systems being a bonus. Candidates should include in their application the following:

  • Academic CV
  • Official academic transcripts
  • Contact details for two suitable referees
  • A personal statement (500 words maximum) outlining their suitability for the position and research experience to date relevant to the project
  • Application form

Email your completed application, and personal details and equal opportunities form, to 

You should attach your application form and all documents to the email (don't use a web-based upload/weblink service) and use the format job reference number / job title / your name in the subject line.

You can also send your application by post to Human Resources Division, Sussex House, University of Sussex, Falmer, Brighton, BN1 9RH.

Download our terms and conditions summary for Research Faculty Terms and Conditions [DOC 36.00KB]

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