Lecturer or Associate Professor in Machine Learning for Atmospheric and Climate Sciences

Are you an experienced and ambitious academic looking for your next challenge? Do you have experience in applying data science methods within atmospheric and climate science research? Do you have an excellent research record and proven success obtaining funding? Do you have experience of delivering an exceptional student experience in a research-intensive Russell Group University?

The School of Earth and Environment (SEE) is seeking applications from enthusiastic and self-motivated data scientists to complement existing expertise in the School. Your role will be to strengthen and expand the use of machine learning and artificial intelligence within research and teaching activities in SEE. We are looking for individuals who are keen to develop and integrate data science training into student education within the School at undergraduate and postgraduate levels. Your role will also be to strengthen and enhance SEE's excellent, internationally recognised profile in atmospheric and climate science research with a special focus on the exploitation of data science methods for scientific discovery. A proven record of research related to data science and atmospheric or climate science is essential. We particularly encourage applicants with interests in applying machine learning to study climate extremes, climate impacts, Earth Observation data and/or ocean-cryosphere interactions, which would build upon existing expertise. Willingness to engage with a wider set of researchers within the School who are applying machine learning to other environmental problems is essential. You will also engage with colleagues in the Centre for Environmental Modelling and Computation to develop a strategy to grow technical support for data science research in SEE.

You will have a PhD in data science, computing, climate science or allied subjects and substantial research expertise in the field of machine learning applications to atmospheric and climate science. You will also have experience of teaching or training in data science, with a track record of diverse collaboration with researchers from multiple disciplines. Finally, you will have experience of both teaching and assessment in a University setting, and of supervision of masters and PhD students.

To explore the post further or for any queries you may have, please contact:

Professor Amanda, Maycock, Director of the Institute for Atmospheric and Climate Science

https://environment.leeds.ac.uk/see/staff/1404/professor-amanda-maycock

Tel: +44 (0)113 343 9793

Email: A.C.Maycock@leeds.ac.uk