Postdoc position: Statistical Data Science and Machine Learning for Climate Change Research — Universität Bern
- Location
- Bern
- Contract
- full-time
- Posted
- 101 days ago
Role overview
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.
- 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.
Application process
- 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. International reputation Application and Contact
- Evaluation of applications will begin on June, 29th 2026, and will continue until the position is filled.
- Application domains encompass climatology, meteorology, hydrology, and societal aspects of climate change research. What you can expect Profile of the candidate More Information Job-Abo Your Benefits
- Strong research infrastructure and international network
- Collaborative environment and ambitious team Individual career support Other benefits Working at the University of Bern
- The University of Bern not only offers exciting tasks but also an environment that actively promotes development, diversity, and equal opportunities.
Contacts
- 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] About the project
- We are seeking a highly motivated and skilled individual to join the Statistical Data Science and Machine Learning Platform lead by Prof.
- Dr.
- David Ginsbourger (Institute of Mathematical Statistics and Actuarial Science) and Prof.
- Olivia Romppainen-Martius (Climate Impacts groups, Institute of Geography) in the framework of the Oeschger Centre for Climate Change Research (OCCR).
- Of particular interest are developments in generative modelling, operator learning, amortized inference, and how recent advances in scientific ML can be leveraged in climate change research, e.g., by combining foundation models with diffusion models for local/specific downstream tasks.
Additional details
- Employment conditions and remuneration are in accordance with the standards of the University of Bern, Switzerland. Profile of the candidate
- The candidate is proficient in English, both written and spoken. International reputation Application and Contact
- 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]
- Application domains encompass climatology, meteorology, hydrology, and societal aspects of climate change research. Application Process
- Collaborative environment and ambitious team Individual career support Working at the University of Bern
- Discover what makes us stand out as an employer and how you can grow with us.
Notes and original content
- Employment conditions and remuneration are in accordance with the standards of the University of Bern, Switzerland.
- Profile of the candidate
- The candidate is proficient in English, both written and spoken.
- International reputation
- Application and Contact
- About the project
- Application domains encompass climatology, meteorology, hydrology, and societal aspects of climate change research.
- More Information
- Application Process
- Your Benefits
Questions about this listing
What salary does Universität Bern offer for this role?
Universität Bern lists CHF 85'000 - 119'000 gross per year for this position in Bern. This is the salary published in the original listing (or, when the employer omits a figure, a realistic estimate for the role and sector) — the calculator on this site converts it to your actual net take-home once cross-border tax and social contributions are applied.
Is this a full-time role, and what type of contract does Universität Bern offer?
This listing is a full-time position. The contract type shown here comes directly from the employer's original posting; always confirm exact hours, notice period and probation length with Universität Bern during the application process, since these details can vary by role even within the same contract category.
Do I need a cross-border work permit for a role in Bern?
EU/EFTA residents living in the border zone of the country adjoining Canton Bern can apply for a G permit; the Swiss employer files it with that canton's migration office after the contract is signed. Border-zone rules and processing times vary by neighbouring country and canton, so confirm the specifics with Bern's cantonal migration office or with HR during the application.
How do I apply for this position at Universität Bern?
Use the "Apply now" button on this page — it links directly to Universität Bern's original listing at jobs.unibe.ch, so your application goes straight to the employer's own applicant-tracking system. Frontaliere Ticino does not collect or forward applications itself.
