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Senior Research Associate in Environmental Data Science

Mathematics & Statistics
Salary:   £34,804 to £40,322
Closing Date:   Tuesday 04 May 2021
Interview Date:   To be confirmed
Reference:  A3333

Applications are invited for a two-year post-doctoral research position to develop cutting-edge machine learning algorithms and decision-making tools to address key environmental science challenges. This position is part of the large-scale £2.6M EPSRC-funded grant “Data Science for the Natural Environment (DSNE)” ( 

This is an exciting opportunity to work as part of a multi-disciplinary team of researchers consisting of computer scientists, statisticians, environmental scientists and stakeholder organisations, working together to deliver methodological innovation in data science to tackle grand challenges around environmental change. This is a prestigious and high profile research programme targeting a paradigm shift in the role of data in environmental science and leading to long-term impact in decision making. 

The DSNE research programme comprises three core methodological themes (integrated statistical modelling, machine learning and decision-making, and virtual lab development), all focused on improving the state of the art in environmental data science. As a DSNE researcher, you will contribute to this effort by developing and applying modern data science tools for environmental applications, and implementing them in virtual labs

This position offers a high degree of independence where the successful candidate will have the opportunity to drive their own research direction, working both independently and in collaboration with other DSNE researchers (over 20 academic staff, 4 other postdocs and 6 PhD students). We are particularly interested in applicants who are excited by working on environmental grand challenges and on the potential of working at the interface between disciplines in addressing these challenges. The research will be varied and exciting, with the potential to shape an emerging field of real importance.

You should have, or be close to completing, a PhD or equivalent degree in statistics or  environmental science with a strong data science component (or closely-related field). You will have a track record of high-quality publications in areas of relevance to the project and the willingness to undertake ambitious and challenging research. An understanding of, and experience of working with, environmental data would be a distinct advantage. For more details, please see the detailed Job Description/Person Specification for this position.

Interested candidates are strongly encouraged to contact Prof. David Leslie in advance of making an application (

You will join us on an indefinite contract however, the role remains contingent on external funding which, at this time is due to come to an end on 3 April 2023. 

We welcome applications from people in all diversity groups, and are keen to discuss job share opportunities with interested candidates. 

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Further details:

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