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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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hydrodynamics and/or N-body simulations in the star and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be
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and high flexibility in where and when you work. Access to HPC resources (including GPU clusters) at Helmholtz, the Leibniz Supercomputing Centre (LRZ), and the Forschungszentrum Jülich (FZJ). Training
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different hardware backends. Design conventional (GPU-based) deep neural networks for comparison. Publish research articles, regular participation in top international conferences to present your work
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on materials science tasks as well as integrate your semantic-AI services into high-throughput GPU/HPC workflows, contributing to data management, metadata structuring, and semantic annotation Collaborate with
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Mathematics (analysis, numerics, modeling) or in a comparable program with a strong mathematical focus and knowledge in, for example, functional analysis as well as the theory and numerics of PDEs. Strong
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benchmark them with a realistic case study. The main focus of the project can develop either more in the mathematical theory of MCMC, the implementation of code for the Jülich supercomputers (GPU/CPU
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part of a degree program. In particular, knowledge about finite-element analysis is an absolute must . Familiarity with iterative solvers , preconditioners , multigrid methods , and mixed-precision
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of microfluidic devices. Simulation for microfluidics. (CFD) High Performance Computing and/or GPU programming for this domain. Machine learning algorithms for this domain Clean energy solutions (e.g., microfluidic
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., based on the 1D or analytical model) Hybrid simulation approach (e.g., which combine CFD and 1D simulations) High Performance Computing and/or GPU programming for this domain Machine learning algorithms