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Karlsruher Institut für Technologie (KIT) | Karlsruhe, Baden W rttemberg | Germany | about 1 month ago
description: The Scientific Computing Center is the Information Technology Center of KIT. The Research Group Exascale Algorithm Engineering of SCC works at the interface of algorithmics, parallel computing, and
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Max Planck Institute for Astrophysics, Garching | Garching an der Alz, Bayern | Germany | 5 days ago
of subgrid models in simulations, in particular for feedback processes Creation of new reference cosmological simulation models of galaxy formation Quantification of modelling uncertainties in simulation
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Max Planck Institute for Evolutionary Biology, Plön | Plon, Schleswig Holstein | Germany | 6 days ago
theoretical models and computer simulations. Adaptation of complex traits is assumed to occur through subtle frequency changes at many loci following a shift in the trait optimum, i.e. polygenic adaptation
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high-resolution, quantitative time-lapse soil property measurements using high-performance, parallel computing. Together with our existing rich dataset, we will inform a soil-plant digital twin, enabling
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integrate linear and circular processes, enabling used products to be transformed into new generations. What you will do Implement GPU-accelerated Gaussian Mixture Model (GMM) learning in PyTorch Optimize
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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for ground-penetrating radar (GPR) and electromagnetic (EM) will be developed. These algorithms will enable high-resolution, quantitative time-lapse soil property measurements using high-performance, parallel
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, transcriptomic, and proteomic approaches. In parallel, engineered Aspergillus niger strains will be developed for the sustainable production of selected pigments as dyes, which will be further scaled up
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engineered 3D hydrogels, we will experimentally probe the mechanical forces and physical constraints that drive coordinated cell behavior. In parallel, we will develop and apply computational models and
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on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training and optimizing the execution User support in