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experiments, particularly ATLAS and DUNE. Contribute to the architecture and core development of the Phlex framework, emphasizing scalable, multi-threaded, and heterogeneous (CPU/GPU) computing models
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. Ubuntu) is essential.. Knowledge of GPU computing, CUDA programming, model optimization, and industry experience in engineering software development would be an advantage. Excellent written and oral
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7 Mar 2026 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Computer science » Informatics Computer science » Programming Engineering » Materials
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Job Code 0005 Employee Class Civil Service Add to My Favorite Jobs Email this Job About the Job The successful applicant will assist in the adaptation of the PPMstar code to run well on GPU-accelerated
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heterogeneous (CPU/GPU) computing models. Collaborate with physicists, computer scientists, mathematicians and engineers across LBNL divisions to define software requirements, implement robust solutions, and
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international journals or conferences. Experience in predictive modeling for forecasting or recommendation systems. Strong programming skills in Python and AI frameworks (PyTorch, TensorFlow), including GPU/cloud
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science or engineering field; a completed PhD is an advantage Very good knowledge of software development with C++ and Python, as well as experience in GPU programming (e.g., CUDA or comparable frameworks
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ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI INGEGNERIA INDUSTRIALE | Italy | 23 days ago
defined Job Status Not Applicable Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer
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, Cloud Service Deployment). Desired: Experience with High-Performance Computing or GPU programming (CUDA). Specialized knowledge of Neural Rendering (NeRF/3DGS) or Satellite Photogrammetry. Demonstrated
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frameworks). Experience using open-source model ecosystems such as Hugging Face (Transformers, Datasets, Accelerate). Experience using or supporting supercomputing or GPU-enabled clusters. Experience with data