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on high-performance computing (HPC) environments using distributed data or model parallel training. Experience with multi-physics simulations on HPC and with ML models. Experience working in a multi
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cell- and module level. Particular emphasis will be placed on leveraging modern heterogeneous computing architectures to enable large-scale, high-fidelity reacting-flow simulations. The models developed
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systems techniques. Proficiency in programming languages such as Python, C++, or similar, as well as experience with HPC environments and parallel computing. Demonstrated hands-on experience and
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contribute to analyses and processing of data. They will become familiar with high performance computing environments and parallel processing, as well as cloud computing infrastructure. They will
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. About us The Department of Computer Science and Engineering , a joint department of Chalmers and the University of Gothenburg, spans the breadth of computing disciplines. Our internationally
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, statistics, machine learning, programming in C, C++, Python Scripting languages and MATLAB, using a parallel computing environment Bioinformatics, network biology, network medicine, network analytics, AI, ML
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; and one-sided asynchronous programming models. Experience in heterogeneous computing, developing and debugging massively parallel algorithms and code performance profiling are a plus. Special
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time-series data Experience of high-performance computing, GPU-based computing, or parallel programming Experience of probabilistic modelling or inverse problems Experience of developing and
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systems for extreme-scale, heterogeneous computing. This role sits at the intersection of compiler infrastructure, formal verification, runtime systems, and hardware/software co‑design—expanding into
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algorithms for analysis of large image datasets. Experience with parallel computing, high-performance computing, distributed computing, and/or cloud-based computational workflows. Experience with large-scale