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and postdocs. In parallel, they will oversee the development and implementation of computational strategies to support and enhance research activities across the institute. Your Responsibilities: Lead
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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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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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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
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sintering press with selected copper pastes, followed by detailed characterization of the resulting interfaces in terms of porosity, thermal and mechanical integrity. In parallel, simulation models will be
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skills Confident working in dynamic environments with a focus on efficiency and prioritizing parallel projects What you can expect Fascinating challenges in a scientific and entrepreneurial setting
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build reliable, reproducible data flows for large EO datasets and workflows Lead performance engineering (parallelization, optimization, benchmarking) for adaptation and inference at scale Work closely
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 1 month ago
to minimize training effort # Devise appropriate metrics to evaluate and tune trained models with respect to reproduction of key physical results # Contribute to a parallel training workflow to stream data from
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and optimize large-scale training and inference runs for foundation models on JUPITER (multi-GPU/node, mixed precision, parallelization, I/O optimization) Integrate multimodal data sources (e.g., scRNA