Machine learning engineer Jobs in Bern
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Machine learning engineer • bern
PostDoc position in scientific machine learning
Universität BernBern, Region Bern, SwitzerlandFachverantwortliche •r Künstliche Intelligenz 80-100%
Berner FachhochschuleBern, Kanton Bern, SchweizWindows DevOps Engineer
Linksolutions AGBern, Canton of Bern, SwitzerlandDevSecOps Engineer (Fortinet)
Coopers Group AGBern, SchweizOpératrice machine H / F
InterimaBerneMarketing Data Analystin
Die Schweizerische PostBern, Bern, SwitzerlandCloud Data Architect mit Flair für AI
ti&mBernData Scientist
Randstad - High priorityKonolfingen- Gesponsert
Software Engineer Automation
Interiman group - FlexsisKönizSoftware Engineer – Machine Learning Integration
Prime21 AGBernMachine Engineer / Servicetechniker (m / w / d)
Adval Tech (Switzerland) AGNiederwangenTalent Community Software Engineering
ThalesBernSenior BeraterIn / Lead Career & Learning
cinfoBern, Bern, SchweizData Scientist
RandstadKonolfingenNetwork Engineer
Michael PageBern, CHDevOps Engineer (22145)
IQ Plus AGBern, Canton of Bern, SwitzerlandDevOps Engineer
Transgourmet Schweiz AGMoosseedorf, Bern, SchweizSoftware Engineer Automation
FLEXSISKöniz, Bern, CHÄhnliche Suchanfragen
PostDoc position in scientific machine learning
Universität BernBern, Region Bern, SwitzerlandThe Space Research and Planetary Sciences Division of the University of Bern's Physics Institute 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