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Knowledge of machine learning, Large Language Models (LLMs), Vision Language Models (VLMs), or generative AI Experience with Retrieval-Augmented Generation (RAG), AI agents, model-driven engineering, DevOps
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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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-based AI for multidisciplinary tumor boards. The successful candidate will develop LLM-based pipelines, semantic harmonization, and transformer-based temporal models on a large multimodal oncology dataset
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environment. Particularly advantageous Strong knowledge of statistics, statistical learning, or probabilistic modeling. Experience collaborating with experimental scientists, biologists, or clinicians. Interest
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. The energy is continuously injected at the level of the individual particles or agents, keeping the system out of equilibrium. Examples of active matter include bacterial colonies, cytoskeleton formed by
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
environment. Desirable qualifications Strong knowledge of statistics, statistical learning, or probabilistic modeling. Experience collaborating with experimental scientists, biologists, or clinicians. Interest
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for more than 3,500 treatment courses, aligned with the OMOP Common Data Model and robust data-quality rules Develop and evaluate agent-based AI functions for therapy-sequence support, clinical-trial matching and
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with reducing and oxidising gas-phase species (e.g. laser-based imaging diagnostics, setup of model reactors, modelling of underlying reactions, multi-scale simulation of reactive fluids, computational