Applications are invited for five fully-funded PhD studentships in which you will learn to develop cutting-edge data science approaches to address key environmental science challenges.
The studentships are part of the large-scale £2.6M EPSRC-funded grant Data Science for the Natural Environment (DSNE), a joint project between Lancaster University and the NERC Centre for Ecology & Hydrology (CEH). This is an exciting opportunity to work at the heart 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.
The DSNE research programme 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 research is arranged around methodological developments in three core methodological themes (integrated statistical modelling, machine learning and decision-making, and virtual lab development), interlocked with three challenge themes from the environmental sciences (ice sheet melt prediction, air quality modelling and land-use modelling). PhD topics are available across the spectrum of DSNE research, including social science aspects, and are listed below.
If learning to develop and deploy data science techniques to solve the biggest problems faced by humanity is an appealing next step, please send a letter of application to Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo. by 5pm on Monday 11th February. Unfortunately, while we can cover the full fees and stipend of UK/EU applicants, the full fee of non-EU applicants cannot be covered. The letter should include
- An ordered list of which of the PhD projects you would like to be considered for, with an explanation of your reasoning
- An explanation of why your skill set and previous education will allow you to be successful in these projects (a transcript of your undergraduate or masters degree programme is likely to be helpful)
To be able to answer these questions sensibly, it is advisable to talk to the supervisors of your desired projects, detailed below, in advance of submitting your application.