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-native wireless communications and networking, specifically developing context-aware multimodal Generative AI (GenAI) models, scalable mixture-of-experts (MoE) at the network edge, and multi-agent
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optimization and LLM alignment: design preference-based training and fine-tuning methods (RLHF, PPO, DPO, reward modeling) for medical and multilingual LLMs. Agentic and tool-augmented AI systems: develop
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, automation, or autonomous systems. - Dynamic modeling and physical simulation. - Trajectory planning, navigation, or robot control. - Interest in multi-agent systems and human-robot interaction. Technical
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fragmentation. This project seeks to overcome these barriers by integrating BIM-based energy modeling, semantic data models (Ontologies), and Large Language Models (LLM) into the control workflow. The candidate
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physical agent-based models, as well as the integrations of omic information to validate model predictions and developed in the context of the HPC environments at the BSC and at other HPC centres in Europe
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a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Molecular Oncology group (http://bit.ly/molonco ) at Institut de Recerca Biomèdica de
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to and build upon modern Python-based statistical modeling tools such as pyhf, supporting likelihood publication, large-scale analysis combinations, and result reinterpretation across the ATLAS
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
software engineering skills in Python, including testing, packaging, and maintainable code development. Experience building or working with agentic systems, LLM-based tool use, or workflow orchestration
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Evolution. Please check our publications for more details: http://garciajulian.com [1] “Empirical Agent Based Models of Cooperation in Public Goods Games | Proceedings of the Fourteenth ACM Conference
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selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, training and internal pay