81 computational-physics "https:" "https:" "https:" "https:" "Caltech" research jobs at Nature Careers in Denmark
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information, please contact Associate Professor Bettina Hjelm Clausen by email bclausen@health.sdu.dk , phone +45 65 50 48 31. Read more about the Department of Molecular Medicine at https://www.sdu.dk/en/om
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a master’s degree (if he/she applies for a position as research assistant) or a master’s and Ph.D. degree (if he/she applies for a position as a postdoc) in Bioinformatics, Computational Biology, or
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Three-Year Postdoc Opportunity in Ecosystem Structure, Functions and Services in Offshore Marine ...
candidates regardless of personal background. Application deadline: 1st of May 2026 at 23:59 hours local Danish time Please see the full call, including how to apply, on https://fa-eosd-saasfaprod1
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genome engineering, quantitative and live-cell microscopy, biochemistry, and computational analysis to dissect how cells sense and respond to replication-associated threats. Recent work from the lab has
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an experience in technology-assisted monitoring or computational image analysis. Expected start date and duration of employment The position will start in June 2026, with exact starting date as agreed between
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. The candidates must hold a PhD in Chemistry/Physics. Experience in data framework development, kinetic/thermodynamic modeling, and collaborative interdisciplinary research. An education history in chemical
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incomplete applications will not be considered. Assessment Shortlisting will be used as part of the initial selection process. Assessment of shortlisted applications will be done under the existing Appointment
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-year period and is funded by a Carlsberg Foundation Accomplish Grant to Prof. Nanna B. Karlsson. About the position This position is part of the research programme REGLA, investigating the physical
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sustainable process development. Opportunities to contribute to an ambitious research program (HyperCap) advancing novel carbon capture technologies toward pilot-scale demonstration. A supportive and
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, and train deep learning models on the resulting data to design new antibiotic compounds that evade both current and likely future resistance mechanisms. Your computational work will directly steer