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focuses on the development of GPU-accelerated, high-fidelity thermal runaway simulation models for lithium-ion battery cells, modules, packs, and complete battery systems. Thermal runaway is a chain
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collaborators and strong access to compute through national GPU systems (NAISS, e.g. Berzelius and Arrhenius) and local GPU infrastructure. Project description The position offers significant scientific freedom
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, e.g in a GPU architecture, and their applications to magnetic phenomena is meriting. About the employment The employment is a temporary position of two years according to central collective agreement
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programming in C/C++, preferably also Rust, and of POSIX, the Linux/Unix kernel or RTOS. Documented competence in parallel and distributed systems, including GPU programming (e.g. CUDA). Ability to explain
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GPU infrastructure. This Postdoc position is part of the eSSENCE graduate school in data-intensive science. The school addresses the challenge of data-intensive science both from the foundational
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such as TensorFlow, PyTorch, or JAX is a must and experience with GPU-based experimentation and cluster computing (e.g., Docker, Slurm) a plus. In addition to the above, there is also a mandatory
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resources and a state-of-the-art in-house lab equipped with NVIDIA H100 and A100 GPUs and ten NVIDIA RTX Pro 6000 GPUs. Qualifications Requirements A doctoral degree or an equivalent foreign degree
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higher education credits (ECTS). Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural networks, as well as courses in Python, GPU programming
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these fields, demand for GPU resources for AI has increased significantly in recent years. We operate modern HPC clusters, visualisation resources and advanced storage systems, and continuously develop new
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software or similar languages and experience with modern machine learning and deep learning frameworks parallel computing using clusters like UPPMAX and GPUs for high-performance computing and parallel