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description of the work duties, such as: Develop and implement GPU-accelerated thermal runaway models and numerical methods in OpenFOAM. Design and optimize computational frameworks for high-performance
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Your Job Design, fabricate, and characterize advanced FDSOI devices for neuromorphic computing applications Develop and optimize semiconductor fabrication processes in a state-of-the-art cleanroom
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 18 hours ago
) for a collaborative project with a starting date of April 1, 2020. The candidate will be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and
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The Department of Chemistry, Section for Chemical Biology invites applicants for a PhD fellowship in chemical methods for half-life extension of therapeutic peptides. The project is part of the
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, with its world leading groups in functional programming and formal methods, provides the ideal environment for research on programming with formal guarantees — a topic of increasing practical importance
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Heilbronn Research Fellowships in Pure Mathematics, Data Science, and Quantum Computing The role The Heilbronn Institute for Mathematical Research invites applications for Heilbronn Research
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National Taiwan Normal University | 2 months ago
invites applications for up to two postdoctoral positions in gravitational dynamics and numerical methods working with Prof. David M. Hernandez. The position is for one year, with renewals up to a
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Mathematics (CCM), in the Flatiron Institute, a division of the Simons Foundation. The mission of the Flatiron Institute is to advance scientific research through computational methods, including data
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Skip to main content Search Menu Norwegian version Job openings PhD fellowship in quantum computing, optimization on noisy gate-based NISQ platforms Location Oslo Apply by 6 November 2026 Position
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the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum