59 programming-language "Data driven Materials Modeling" Postdoctoral positions in Switzerland
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Abstractions Lab (LASeR) develops programming languages and formal reasoning tools for networked systems. Networks are the invisible infrastructure that connects the modern world, yet programming and reasoning
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languages, systems, or ML Research experience in one or more of: interactive theorem proving (Rocq, Lean, HOL, Isabelle, or similar), GPU programming or semantics, compilers, concurrency, or ML systems (e.g
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characterization and process-evaluation methods for end-of-life PV modules, assessing recovery yield, purity, energy consumption, environmental impact, scalability and economic relevance. You will plan and conduct
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Positions Application Deadline 15 Oct 2026 - 15:55 (Africa/Abidjan) Country Switzerland Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not
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of Contract Permanent 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 position within a Research Infrastructure? No
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surface science Prior experience with piezoelectric- or ferroelectric nitrides is an advantage Knowledge of PVD process development and pulsed sputtering is an advantage Proficiency in Python programming
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Programme? Horizon Europe Is the Job related to staff position within a Research Infrastructure? No Offer Description Materials science and technology are our passion. With our cutting-edge research, Empa's
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the EU 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
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-ended research questions You communicate well in English and enjoy working at the boundary between disciplines We offer At EPFL, we believe that people achieve great results when they feel supported
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. • Excellent Python programming skills and strong hands-on experience implementing, training, and evaluating deep-learning models and research codebases. A strong track record in machine learning or closely