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Field
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performance GPU computing — whether through thousands of commercial GPUs or a handful of Nvidia NVL72 racks — and specialized signal processing hardware. Automation: Develop and implement automated processes
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systems and underwater imaging. 4. Linux system administration and scientific computing workflows, including environment management, GPU-accelerated workloads, and remote/headless operation. 5. Familiarity
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DeepMD, NEXMD, i-PI, MixPI, NWChem, CP2K, or comparable packages. Experience with GPU acceleration, distributed training, high-performance computing systems, or C/C++ scientific software development
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computing environments and GPU computing. Proven experience in weather and climate models development and applications. Experience in machine learning, deep learning, or AI applications for atmospheric
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compensation and working conditions. Basic Qualifications Ph.D. or M.D./Ph.D. in areas such as machine learning, computer science or closely related field. Excellent programming skills and practical experience
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Laboratory of Photonics, Electronics, and Quantum
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functioning in a dynamic technological environment. Preferred Qualifications: Experience with parallel computing, GPU operation (CUDA Toolkit), multi-GPU training, and distributed frameworks for machine
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Postdoc Position in AI Foundation Models for Crop Microbiomes Faculty: Faculty of Science Department: Department of Information and Computing Sciences Hours per week: 36 to 40 Application
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, storage systems, networking, scripting, and infrastructure automation. Experience with GPU computing environments, containerisation technologies, cloud platforms, and scientific software environments will
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for career development Access to high-performance computational resources (with GPUs) A collaborative environment across research fields, including plant biology, quantitative genetics, and population genetics