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previously acquired skills. It extends conventional reinforcement learning by addressing catastrophic forgetting, knowledge transfer, and adaptation under non-stationarity. Potential application domains
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assurance, data science, data-driven modelling, digital manufacturing workflows, and Digital Product Passport B.3 Hands-on experience in machine learning, ontologies and knowledge graphs, IIoT and digital
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selection criteria Knowledge/experience with control engineering, information fusion and/or data assimilation, marine technology Knowledge of and hands-on experience with machine learning and/or statistical
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countries (HIC). The airm of this PhD project is to understand how E. coli colonization contributes tot he risk of diarrheal disease and infections with AMR and use this knowledge to design interventions
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 2 months ago
sound knowledge of statistics, machine learning, image analysis, or spatial data analysis The ability to assess algorithms and models not only from a technical perspective, but also in terms
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applications, spreadsheets, and data management tools. Ability to work independently while contributing effectively to a multidisciplinary research team. Preferred Qualifications Experience coordinating
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). Desirable assets are: • Machine learning, deep-learning, artificial intelligence, advanced statistical inference; • A solid record of research activities, including relevant publications in international peer
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applicability. By bridging machine learning, behavioural science, and clinical research, the project seeks to establish foundational methods for trustworthy agentic AI systems that can be deployed across diverse
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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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-Physical Energy Systems The PhD position focuses on the development of secure and trustworthy AI for resource-constrained embedded systems. The research will investigate how machine learning models can be