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, especially in quantitative subjects • Strong Python skills and experience with deep learning frameworks, preferably PyTorch • Solid foundations in machine learning, statistics, linear algebra, and model
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optimal computing and communication architectures for hardware acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware
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learning-based surrogates for physical systems LLMs and scientific agents – large language models that autonomously reason, plan and execute scientific workflows AI for engineering design – LLM-driven agents
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and experience with modern deep learning frameworks (e.g. PyTorch) Solid background in machine learning, ideally with experience in NLP, large language models, or sequence modeling Interest in clinical
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exciting opportunity for you to join our team working on Scientific Machine Learning (SciML). The project focuses on developing scalable HPC algorithms and mixed-precision solvers to train neural models
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or complementing traditional physics-based approaches by data-driven ones, using Machine-Learning (ML). Such approaches allow enormous gains of time, in a way that can be related to the astonishing efficiency
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Campus (LLEC). Development of physics-informed and graph-based machine learning methods for energy system monitoring, forecasting, and planning Data analysis considering uncertainties, missing data
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-cell imaging, multi-omics profiling such as transcriptomics, proteomics, and metabolomics, single-cell and spatial analyses, multiplex biomarker quantification, functional studies, machine learning
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 2 months ago
and advance statistical, image analysis, and machine learning approaches Develop interpretable models that reveal biologically testable relationships and generate new scientific hypotheses Independently
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, and machine learning methods, choosing the approach that best fits the scientific question. Investigate systematically what information is contained in imaging data, how it can be extracted, and how