Postdoc position: Statistical Data Science and Machine Learning for Climate Change Research

Postdoc position: Statistical Data Science and Machine Learning for Climate Change Research

UniBE is ambitious. With us, you deliver top performance and test your skills against the best.

What you can expect

Together with the platform leads and members, the recruited person will actively engage in platform coordination and scientific animation activities, provide expertise and feedback to graduate students and other Early Career Researchers. Scientific animation may include crash-courses on topics relevant to the platform and the recruited person. With a baseline affiliation in statistics, the recruited person will be encouraged to regularly visit and possibly be hosted (part time) in other OCCR groups depending on collaborations.

Employment conditions and remuneration are in accordance with the standards of the University of Bern, Switzerland. The University of Bern is committed to promoting equal opportunities and equality between women and men and all genders, strives to reduce barriers to entry and promotes the reconciliation of work and care responsibilities. For the university, the diverse perspectives of university members are an essential component of excellence in research, teaching and administration.

Profile of the candidate

The successful candidate holds a PhD in statistics / ML / applied mathematics and has a strong interest in climate and weather applications, or he/she holds a PhD in climate or atmospheric science, meteorology or similar with an excellent background in statistics and ML. She/he has excellent programming and data handling skills, and a keen interest in interdisciplinary research. The candidate is proficient in English, both written and spoken.

Application and Contact

Evaluation of applications will continue until the position is filled. Please send a single pdf including your CV, motivation letter, a short (max 4 pages) proposal emphasizing the methodological research directions you envision (ideally echoing the broader topic of generative models as decoders for climate foundation models) and how they relate to the one or several OCCR groups from the platform, as well as contact information of 3 references to [email protected]