tl;dr–we do global health and ML research and use Stan heavily. Maybe you or someone you know wants to come work with us in the Department of Computer Science at Oxford? Apply here: Job Details
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We are seeking a Research Associate to work in the emerging field of spatiotemporal statistical machine learning with applications in public policy.
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, Imperial College London, University of Copenhagen, and Singapore. Reporting directly to Professor Seth Flaxman (Oxford), and collaborating closely with Professor Samir Bhatt (Copenhagen) you will help lead an ongoing multiyear programme of methodological research (2020-2025), to tackle pressing public policy problems in collaboration with leading international organisations.
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. In addition to research, you will help train practitioners in partner organisations. You will also have the opportunity (if desired) to participate in teaching and in the supervision of undergraduate and postgraduate students.
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.
We would particularly welcome applications from the global South, women, black and minority ethnic applicants who are currently under-represented within the Computer Science Department.
The closing date for applications is 12 noon on 18 July 2022. Interviews are expected in July 2022.
We are a Stonewall Top 100 Employer, Living Wage and Mindful Employer, 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 Department of Computer Science, University of Oxford , 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.