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difference in the transition towards a sustainable future. Subject description This project focuses on the development of GPU-accelerated, high-fidelity thermal runaway simulation models for lithium-ion
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computation, communication and storage, optimizing GPU utilization, parallelization, inter-node communication and I/O performance on parallel file systems. You will develop solutions for monitoring
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and high-performance computing. The work will involve extensive use of high-performance computing resources, including both GPU- and CPU-based computing platforms. Being a doctoral student As a doctoral
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matching, diffusion), simulation-based inference, and robust and active learning. The group has a wide network of collaborators and strong access to compute through national GPU systems (NAISS, e.g
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AI models on HPC and GPU-based systems Provide guidance on performance optimization, scaling, and efficient resource utilization Contribute to architectural and design decisions in cloud-native HPC
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, engineering, medicine, economics, humanities and social sciences. Across these fields, demand for GPU resources for AI has increased significantly in recent years. We operate modern HPC clusters
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tightly coupled to GPU-accelerated high-performance computing using Lund University’s national e-infrastructure. You will join an interdisciplinary research environment that values scientific independence
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Experience with development of atomistic- or multiscale simulation methods, e.g in a GPU architecture, and their applications to magnetic phenomena is meriting. About the employment The employment is a
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environment with a wide network of collaborators, generous support for conference travel, and access to national HPC resources (NAISS) and local GPU infrastructure. This Postdoc position is part of the
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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