Postdoc: Bayesian Data Science for Health Challenges

Overview

Apr 6, 2018

Postdoctoral Research Positions (5 Posts)

New Approaches to Bayesian Data Science: Tackling Challenges from the Health Sciences

Open Positions

• Research Associate, School of Mathematics, University of Bristol
• Research Associate, MRC Biostatistics Unit, University of Cambridge
• Senior Research Associate, Department of Mathematics and Statistics, Lancaster University
• Postdoctoral Research Assistant, Department of Statistics, University of Oxford
• Research Fellow, Department of Statistics, University of Warwick

Project and Focus

We invite applications for Post-Doctoral Research Associates to join the New Approaches to Bayesian Data Science: Tackling Challenges from the Health Sciences programme. This is an exciting, cross-disciplinary research project that will develop new methods for Bayesian statistics fit-for-purpose to tackle contemporary Health Science challenges, including real-time inference and prediction for large-scale epidemics, and synthesising information from distinct data sources for large-scale studies such as the UK Biobank. Methodological challenges include making Bayesian methods scalable to big data and robust to model errors.

This £3M programme is funded by EPSRC and brings together research groups from the Universities of Lancaster, Bristol, Cambridge, Oxford and Warwick. Five PDRA positions are available to build a team that will develop new Bayesian and Computational Statistical methods, motivated and applied to Health challenges. Active collaboration across partner institutions and with project partners is expected. Each partner institution has either a 3-year or 4-year position available.

Responsibilities and Qualifications

• PhD in Statistics or a related discipline (or close to completion).
• Collaborate with investigators to undertake and support research to achieve the grant’s aims at your university.
• Publish in leading statistical/application journals; present research at workshops and conferences.
• Develop code implementing new statistical methods; participate in project meetings.
• Experience in one or more of the following areas: Bayesian statistics, computational statistics, statistical machine learning, statistical genetics, inference for epidemics.
• Proven ability to develop new statistical methodology.
• Strong computational skills; ability to contribute to theoretical, methodological and applied statistics as part of a balanced team.
• Demonstrable ability to produce high-quality academic writing.

Application Process

The application process is administered by Lancaster University for the Lancaster, Oxford, Bristol and Warwick posts. Applicants must apply through Lancaster University’s website: https://hr-jobs.lancs.ac.uk/Vacancy.aspx?ref=A2232, and clearly indicate the position(s) for consideration. For the Cambridge vacancy, go to http://www.jobs.cam.ac.uk/job/16971/ and use the online application form after logging in.

Contact and Diversity

Candidates considering applying are encouraged to contact: Professor Paul Fearnhead (p.fearnhead@lancaster.ac.uk), Sylvia Richardson (sylvia.richardson@mrc-bsu.cam.ac.uk), Christophe Andrieu (c.andrieu@bristol.ac.uk), Chris Holmes (c.holmes@stats.ox.ac.uk) or Gareth Roberts (Gareth.O.Roberts@warwick.ac.uk) to discuss the programme in greater detail.

We welcome applications from people in all diversity groups.