February 2018
Message 62

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[Met-jobs] Four 4-year senior postdoc positions in Environmental Data Science | Lancaster University, UK

From "Young, Paul" <>
To "" <>
Date Mon, 19 Feb 2018 15:09:53 +0000

Environmental Data Science - 4 x Senior Research Associate Positions (4-years)
Lancaster University, Lancaster, UK

Closing date: Sunday 18 March, 2018
Salary: £33,518 - £38,832 (Grade 7)

The positions
Spatio-temporal extremes (Ref A2214)
Virtual Labs in the Cloud (Ref A2211)
Learning and Optimisation (Ref A2212)
Integrated statistical modelling (Ref A2213)

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

Depending on the position, a PhD or equivalent degree in statistics or computer science (or closely-related fields) are required to be eligible. 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. See the job individual job descriptions above for further information.

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

General information - Data Science of the Natural Environment
Four post-doctoral research positions, each of four years, are available in an exciting, cross-disciplinary programme of research to develop and deploy a data science of the natural environment. The project comprises data scientists, environmental scientists and a range of stakeholders, and will focus on methodological innovation in data science to tackle grand challenges around environmental change. This work is funded by the UK EPSRC under their New Approaches to Data Science call. This is a prestigious and high profile award targeting a paradigm shift in the role of data in environmental science and in associated decision making. 

The research programme focuses on integrating spatio-temporal statistical models and extreme value methods, with Gaussian process emulation of deterministic and stochastic environmental process models, and Bayesian optimisation and other machine learning methods leading through to decision support. All methodology will be deployed in a newly developed and open source virtual lab environment. 

You will develop novel approaches that address the particular data science demands in terms of understanding and managing the natural environment. The research will be driven by selected environmental grand challenges in the areas of ice sheet melt prediction, air quality modelling and land use management.

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.

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