164 parallel-programming-"University-College-Cork" positions at Technical University of Denmark
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Job Description Are you driven to shape global health policy and lead impactful international development programs? Do you have a passion for advancing antimicrobial resistance (AMR) solutions and a
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patients facing depth-electrode testing, which is invasive and expensive. The opportunity: We've just shown that parallel transmit 7T MRI produces the best images for detecting epilepsy lesions (see our
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computing Strong programming skills in C++ and experience with parallel computing (e.g., CUDA, MPI). Experience with tokamak edge gyro-fluid modelling Experience with continuous integration of scientific
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Job Description Producing green fuels via Power-to-X (PtX) technologies demands large volumes of ultrapure water. In parallel, the transition toward bioenergy and biogas production is intensifying
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diffusion techniques to design materials with targeted optical properties, scaling to large systems through efficient representations and GPU parallelization. We will also create multi-fidelity predictive
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Research Framework Programme? Horizon Europe - MSCA Reference Number DC6 Marie Curie Grant Agreement Number 101225914 Is the Job related to staff position within a Research Infrastructure? No Offer
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Is the job funded through the EU Research Framework Programme? Horizon Europe - MSCA Reference Number DC5 Marie Curie Grant Agreement Number 101225914 Is the Job related to staff position within a
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commercialized by a DTU spin-out (Spectroinlets) - but we have to move beyond what we already have in order to enable automated detection of non-volatile products. This will be your main objective. In parallel
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to plan and carry out complicated tasks and to pursue parallel paths able to accommodate strict time boundaries imposed by the overall project goals excellent at communicating and reporting your work in
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this project, we will develop neural diffusion techniques to design materials with targeted optical properties, scaling to large systems through efficient representations and GPU parallelization. We will also