PostDoc position in scientific machine learning

of the University of Bern is seeking candidates for a PostDoc to work on Rosetta legacy mass spectrometer data obtained at comet 67P / Churyumov-Gerasimenko (67P). The position, funded by the Swiss National Science Foundation (SNSF), is nominally for 1 year with the possibility of an extension.

The intended start date falls within the May-July 2026 timeframe.

The Bern-led high-resolution Double Focusing Mass Spectrometer (DFMS) aboard ESA's Rosetta mission to comet 67P revealed an unexpected chemical diversity and complexity in cometary matter. The research project uses DFMS legacy data as unique testbed to investigate cometary abiotic organic complexity and establish references for ongoing and future space missions – particularly those employing mass spectrometers in the search for signs of life.

Tasks The work combines physical and chemical knowledge with machine learning (ML) algorithms to reduce and interpret the full mission DFMS legacy data. A central focus lies on the exploration of unsupervised ML methods to support the investigation and characterization of complex organic molecules.

The PostDoc will be working in a multidisciplinary environment at one of the leading houses for space mission instrumentation in Europe and have the opportunity to present their research at international conferences and workshops. Requirements The position formally requires a PhD degree in physics, (astro)chemistry, or related fields, obtained no more than one year ago. We are looking for candidates with extensive scientific machine learning expertise and strong programming skills. Experience in organic chemistry, mass spectrometry, primitive solar system bodies is a plus. We offer The salary will be determined according to SNSF regulations. Rules according to the University of Bern and Canton of Bern apply. Childcare allowance is available.

The Canton of Bern offers 25 days of vacation per year. Public holidays (e.g., Christmas, New Year, Easter, 1 August, etc.) come in addition.

The University of Bern is an equal opportunity employer committed to diversity in its workplace, and applications from under-represented groups are encouraged. Application Interested applicants are invited to submit a curriculum vitae (including details of professional experience and skills), a one-page motivation letter, contact information for two referees, and a transcript of their PhD diploma to Dr. Nora Hänni :