30 computer-"https:"-"APOS-UFFICIO-CONCORSI-DOCENTI" "https:" "https:" "https:" "https:" "J. F" positions at Empa
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(R1) Country Switzerland Application Deadline 10 Feb 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Horizon Europe
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Deadline 6 Feb 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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Switzerland Application Deadline 20 Jan 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Materials science and technology are our passion. With our cutting
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Materials science and technology are our passion. With
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, presentations and scientific publications Your profile You have a PhD degree (or equivalent) in Acoustics or Electrical/Mechanical Engineering or Computer Science. Experience with scientific computing in Matlab
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31 Oct 2025 Job Information Organisation/Company Empa Research Field Computer science » Programming Computer science » Other Engineering » Control engineering Researcher Profile Recognised
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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
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in Mechanical or Aerospace Engineering, Physics, Computational Science, or a related discipline. The candidates should have: Solid programming skills (Python, MATLAB, or C++). Knowledge of the OpenCV
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to support a circular bioeconomy. We are seeking a highly motivated postdoctoral researcher to develop and apply machine‑learning and data‑science methods, with a focus on computer vision, to enhance wood