22 high-performance-quantum-computing-"https:"-"Simons-Foundation" PhD positions at Empa
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submit these exclusively via our job portal. Applications by e-mail and by post will not be considered. Where to apply Website https://academicpositions.com/ad/empa/2025/phd-position-in-computational
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continuum modeling (finite element modeling, computational fluid dynamics), and proven experience with COMSOL Multiphysics. Knowledge of heat and mass transport processes in heat-sensitive materials and
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. The candidate should have a strong background in computational biology or a related field. They will: Work closely with the clinical partner to understand the unmet clinical needs. Integrate the existing datasets
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15 Jan 2026 Job Information Organisation/Company Empa Research Field Computer science » Other Engineering » Other Mathematics » Applied mathematics Mathematics » Statistics Researcher Profile First
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First Stage Researcher (R1) Country Switzerland Application Deadline 23 Mar 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme
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of industrial high-temperature processes with potential for negative carbon emissions. PhD student in the field of numerical simulation of plasma-based methane decomposition Your tasks Setup of Computational
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15 Nov 2025 Job Information Organisation/Company Empa Research Field Computer science » Other Environmental science » Earth science Environmental science » Other Researcher Profile First Stage
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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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performs research on nanomaterial engineering, micro-optics and microfluidics, which provides a wide range of opportunities for the PhD candidate. Your profile We are looking for suitable candidates with a
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flow reconstruction, enabling both real-time coarse diagnostics and high-fidelity offline velocity field estimation. Developing reinforcement learning (RL) algorithms for a multi-agent robotics system