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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description With the increasing complexity of numerical simulation codes, new
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steady and transient state, at scales ranging from nanometres to millimetres. Develop numerical methods to capture droplets evaporative behavior accurately Compare and validate numerical results with
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and tuning. Moderate research project experience training large-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain
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project experience training large-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain expertise in areas like computational
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algorithms in the context of sparse tensor operations and apply them to real-world datasets. Parallel Computing: Explore opportunities for parallelism in the tensor completion process to enhance computational
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from nanometres to millimetres. Develop numerical methods to capture droplets evaporative behavior accurately Compare and validate numerical results with experimental data from both literature and in
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tight AI-simulation coupling. What is Required: PhD in Physics, Chemistry, Computational Science, Data Science, Computer Science, Applied Mathematics, or a related numerical field. Programming experience
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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substantial knowledge and research experience in areas such as computational fluid dynamics, turbulence modeling, data-driven methodologies, machine learning, and parallel computing. The candidate should also
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substantial knowledge and research experience in areas such as computational fluid dynamics, turbulence modeling, data-driven methodologies, machine learning, and parallel computing. The candidate should also