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science research conferences. Qualifications: PhD in computer science with file systems, GPU architecture experience. Proven ability to articulate research work and findings in peer-reviewed
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optimisation Experience in one or more of the following areas would be advantageous: EO or geospatial data analysis, parallel or distributed computing, GPU programming, Linux and containerised
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programming, GPU programming) Astrophysical fluid dynamics and/or magnetohydrodynamics Radiative transfer in astrophysics Machine learning or data analysis for physical sciences Strong programming
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coursework (examples): Computational physics/scientific computing and numerical methods for PDEs High‑performance computing (parallel distributed programming, GPU programming) Astrophysical fluid dynamics
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) environments, GPU-enabled systems, and cloud-based resources for large-scale data analysis and model development. Using these rich data resources, we develop and apply advanced AI/ML methods to model tumor
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Position Description An Associate in Research position is available in the Interpretable Machine Learning Lab ( https://users.cs.duke.edu/~cynthia/home.html ) for a scientific developer to work in
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distributed systems, including GPU programming (e.g. CUDA). Ability to explain complex, abstract and hardware-related concepts to students in a structured and accessible way. Documented experience of
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Computer science » Programming Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) Established Researcher (R3) Application Deadline 25 Oct 2026 - 23:59 (UTC) Country Belgium Type of
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systems • Strong computational background Preferred Qualifications • Experience with GPU programming, shaders, or advanced rendering techniques • Experience integrating external APIs or live data
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and optimisation Experience in one or more of the following areas would be advantageous: EO or geospatial data analysis, parallel or distributed computing, GPU programming, Linux and containerised