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Senior Research Associate

Lancaster University - Mathematics & Statistics

Location: Lancaster
Salary: £34,189 to £39,609
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 28th November 2018
Closes: 4th January 2019
Job Ref: A2497

Contract: Full time, four year fixed term post

Applications are invited for a four-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). As a DSNE researcher, you will develop novel methodological advances under the machine learning and decision-making theme, with an opportunity to deliver real-world impact through applying your research in the grand challenge theme areas (ice sheet melt prediction, air quality modelling and land-use management). For this position, we are particularly interested in appointing someone with an interest in areas such as Bayesian optimisation, Gaussian processes, active learning and probabilistic modelling, who is keen to apply their work in the context of environmental models to facilitate decision-making by both environmental scientists and policymakers.

This position offers a high degree of independence where the postdoctoral researcher can follow their own research direction and work closely with other DSNE researchers (over 20 academic staff, 4 postdocs and 5 PhD students). Experience of working with environmental data would be an advantage but not essential. 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, Machine Learning, Data Science (or a 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. 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 (

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

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