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Research Associate in Physically-Informed Probabilistic Modelling of Air Pollution for the Global South

University of Sheffield - Department of Computer Science & School of Mathematics and Statistics

Location: Sheffield
Salary: £31,302 to £39,609 per annum (Grade 7)
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 20th August 2019
Closes: 19th September 2019
Job Ref: UOS023634
 

Contract Type: Fixed-term for a period of 24 months with a planned start date of October 1st 2019.

Faculty: Faculty of Science

Department:

Are you interested in working for a world top 100 University?

An exciting opportunity has arisen for a Research Associate to work jointly in the School of Mathematics and Statistics and the Department of Computer Science on a new EPSRC funded project on 'Physically-informed probabilistic modelling of air pollution in Kampala using a low cost sensor network'. This is a collaborative project between the University of Sheffield and the University of Makerere, and will develop and deploy machine learning methodology to analyse air pollution data from Kampala, in order to determine the source of the pollution and to aid the design of mitigating interventions.

The project team will consist of three Research Associates, two in Kampala, one in Sheffield. The Sheffield research associate will be supervised by Professor Richard Wilkinson in the School of Mathematics and Statistics and Dr Mauricio Alvarez Lopez in the Department of Computer Science, and will focus on the development of the mathematical tools needed to incorporate physics into machine learning models, and on the development of inferential approaches for these models that are able to deal with large amounts of noisy data.

You will have, or be about to obtain, a PhD in statistics or theoretical/computational physical sciences, and will have some experience of machine learning. You should also have effective communication skills, a desire to be part of an international multi-party collaboration, and be willing to travel to Kampala (approximately one trip per year). You will need to develop novel methodology in the field of Gaussian processes/spatio-temporal modelling and uncertainty quantification. In particular, you will be developing machine learning methodology that incorporates domain-knowledge about diffusion and advection (of air pollution), as well as other physically-informed constraints for spatio-temporal data. As part of this project, you will implement inferential methods for these models, such as variational inference, and Hamiltonian Monte Carlo.

This is project is funded by the EPSRC Global Challenges Research Fund (GCRF) which supports cutting-edge research to addresses the challenges faced by developing countries.

The University of Sheffield is one of the best not-for-profit organisations to work for in the UK. The University’s Total Reward Package includes a competitive salary, a generous Pension Scheme and annual leave entitlement, as well as access to a range of learning and development courses to support your personal and professional development.

We build teams of people from different heritages and lifestyles from across the world, whose talent and contributions complement each other to greatest effect. We believe diversity in all its forms delivers greater impact through research, teaching and student experience.

To find out what makes the University of Sheffield a remarkable place to work, watch this short film:

www.youtube.com/watch?v=7LblLk18zmo, and follow @sheffielduni and @ShefUniJobs on Twitter for more information.

   
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