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Research Associate in Computational Statistics and Machine Learning

University of Oxford - Computer Science

Location: Oxford
Salary: £36,024 to £44,263 p.a. with the potential to under-fill at Grade 6 with salaries in the range of £32,332 - £38,205 p.a, Grade 7
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 30th January 2024
Closes: 8th March 2024
Job Ref: 170645

Fixed Term contract for 1 year

We are seeking a Research Associate to work at the intersection of computational statistics, machine learning, and global health.

You will be based in the Department of Computer Science at Oxford and a member of the Machine Learning & Global Health Network, a multi-institution research laboratory with members at Oxford, Bristol, Imperial College London, University of Copenhagen, and Singapore. Reporting directly to Professors Seth Flaxman and Mark van der Wilk (Oxford), and collaborating closely with Prof Samir Bhatt (Copenhagen/Imperial) you will help lead an ongoing multiyear programme of methodological research, to tackle pressing global health problems in collaboration with international organisations including the World Health Organization and the World Food Programme.

You will regularly collaborate with researchers in the Machine Learning & Global Health Network; key external partners including the World Health Organization, the US Centers for Disease Control and Prevention, the Stan Development Team, the World Food Programme, UNAIDS, and NASA; internal partners at Oxford, including the Computational Statistics & Machine Learning group in the Department of Statistics, the Pandemic Sciences Institute, the Big Data Institute, and the Centre for Evidence-Based Social Intervention in the Department of Social Policy and Intervention.

You will be expected to communicate research findings to other researchers, through conference and journal publications, and policymakers, through international meetings; demonstrate research independence in the conception and execution of methodological research; disseminate replicable and reproducible data scientific workflows and help train practitioners in the use of new methods.

You should hold a PhD (or be close to completion) in computer science, statistics or a related discipline (e.g. epidemiology, mathematics, physics, environmental science) and possess sufficient specialist knowledge across some/all areas of: machine learning, kernel methods, neural networks, spatial statistics, Bayesian statistics, computational statistics, and probabilistic programming.

Whilst the role is a Grade 7 position, we would be willing to consider candidates with potential but less experience who are seeking a development opportunity, for which an initial appointment would be at Grade 6 (Grade 6: £32,332 - £38,205 p.a.) with a title of Research Assistant with the responsibilities adjusted accordingly. This would be discussed with applicants at interview/appointment where appropriate.

We would particularly welcome applications from women and black and minority ethnic applicants who are currently under-represented within the Computer Science Department.

The closing date for applications is 12 noon on 8 March 2024. Interviews are expected in March 2024.

We are a Stonewall Top 100 Employer, Living Wage, holding an Athena Swan Bronze Award, HR excellence in Research and Race Equality Charter Bronze Award.

Our staff and students come from all over the world and we proudly promote a friendly and inclusive culture. Diversity is positively encouraged, through diversity groups and champions, for example http://www.cs.ox.ac.uk/aboutus/women-cs-oxford/index.html , as well as a number of family-friendly policies, such as the right to apply for flexible working and support for staff returning from periods of extended absence, for example shared parental leave.

Demonstrating a commitment to provide equality of opportunity. We would particularly welcome applications from women and black and minority ethnic applicants who are currently under-represented within the Computer Science Department. All applicants will be judged on merit, according to the selection criteria.

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