Machine learning engineer Jobs in Köniz
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Machine learning engineer • koniz
PostDoc position in scientific machine learning
Universität BernBern, Region Bern, Switzerland- Gesponsert
- Neu!
Network Engineer
Swatch Group ServicesBerne, SwitzerlandFachverantwortliche •r Künstliche Intelligenz 80-100%
Berner FachhochschuleBern, Kanton Bern, SchweizOpératrice machine H / F
InterimaBerneMarketing Data Analystin
Die Schweizerische PostBern, Bern, Switzerland- Gesponsert
- Neu!
Platform Engineer
Axians Amanox AGBern, CHCloud Data Architect mit Flair für AI
ti&mBern- Gesponsert
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Constructeur mécanique
HE Helvetic Emploi Nord Romand SABerne, Switzerland- Gesponsert
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DevOps Engineer
Sontex SABerne, Switzerland- Gesponsert
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Manufacturing Engineer
AXEPTA SABerne, CH- Gesponsert
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Devops Engineer
Transgourmet Schweiz AGMoosseedorf, CHSolution Engineer
Randstad (Schweiz) AGBernSenior Research Engineer Multimodal & Video Foundation Model (100% Remote)
Tether Operations LimitedBerne, BE, CHSoftware Engineer – Machine Learning Integration
Prime21 AGBernMachine Engineer / Servicetechniker (m / w / d)
Adval Tech (Switzerland) AGNiederwangenSenior BeraterIn / Lead Career & Learning
cinfoBern, Bern, SchweizNetwork Engineer
Michael PageBern, CH- Gesponsert
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HR BUSINESS PARTNER & LEARNING PROJECT MANAGER
Omega SABerne, Switzerland- Gesponsert
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Fachverantwortliche •r Künstliche Intelligenz
Berner Fachhochschule BFHBern, CHPostDoc 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