Senior Research Associate in Environmental Data Science, Spatio-temporal Extremes
Mathematics & Statistics
Salary: £34,804 to £40,322
Closing Date: Sunday 13 October 2019
Interview Date: To be confirmed
Applications are invited for a three-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)” (http://www.lancaster.ac.uk/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 focus on the development of new methodology for spatiotemporal extreme value statistics, and to interface these methods with environmental process models. For this position, we are particularly interested in appointing someone with research experience in developing and applying either extreme value methods, or spatiotemporal methods, and an ability to implement these methods in software. Experience of working with environmental data would be an advantage but not essential.
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 (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. 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 (email@example.com).
We welcome applications from people in all diversity groups, and are keen to discuss job share opportunities with interested candidates.