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than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries. Postdoctoral Positions in Computational Neuroscience & AI Mission The A. Mathis Group for
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than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries. Postdoc position in 3D Computer Vision, remote sensing ... The Laboratory of Visual Intelligence
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, depending on their expertise and interest. They will also support parallel developments led by PhD students, ensuring coherence in the beam dynamics models and helping to define tolerances for overall design
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than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries. Postdoctoral Position in Computational Genomics, Machine Learning & Single-Cell Biology Mission
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and in computer architecture domains, our ability to analyze data still falls behind the unstoppable data collection rates. Data-intensive applications are increasingly more demanding in sophisticated
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to advance knowledge in the following domains: Formal verification and interactive theorem proving (Rocq, Lean) Secure and high-performance computer systems, including ML infrastructure We seek outstanding
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, electron-beam lithography, theory of light-matter interaction, computer simulations using the method of Finite Differences in the Time Domain (FDTD), determination of optical material constants by multiangle
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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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looking for a postdoctoral researcher in the area of Generative AI. VITA research interests lie at the intersection of Computer Vision, Machine Learning (Deep Learning), and Human-Robot Interaction
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, implementation, and open-source release of the lab’s tools and artefacts Participate in the supervision of PhD and master students Profile PhD (or nearing completion of) in computer science or a closely related