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Swiss National Science Foundation (SNSF)-funded project on human motivation, stress physiology, immersive behavioral testing, and multimodal data analysis The Laboratory of Behavioral Genetics at EPFL
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candidates working in formal methods and/or computer systems with interests in one or more of the following areas: machine-checked verification of systems software, GPU kernel semantics and verification, and
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or automated theorem proving, network verification, routing protocols, packet scheduling, or applications of AI/ML to systems and security Strong computational and analytical skills, including solid software
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thermal characterization of prototype devices, including measurements under vacuum and cryogenic conditions Integration and testing of probes in scanning probe instrumentation Collaboration with academic
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Swiss National Science Foundation (SNSF)-funded project on human motivation, stress physiology, immersive behavioral testing, and biological phenotyping. The Laboratory of Behavioral Genetics
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algorithms to store, manage, and interpret data. To meet the challenges, database software needs to communicate efficiently both with the application layer and with the underlying hardware platforms, and
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this new hybrid network. Your code will directly enable the next generation of energy-efficient AI clusters. Project scope You will bridge the gap between custom optical hardware and standard AI software
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before the starting date; • a strong and demonstrated background in statistical mechanics; • experience with both analytical and computational methods. Experience with multiphysics simulation software
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building envelope performance Development of standardised test protocols for non-destructive assessment of building envelope elements Support for ongoing practical research on design for disassembly and
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For both positions: Conduct innovative, independent research aligned with the lab's mission and the candidate's interests Contribute to the lab's open-source software, datasets, and benchmarks Mentor PhD and