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in high-tech, medical, and energy systems. Information Modern engineering increasingly relies on data-driven models to describe complex dynamical systems. Scientific machine learning is now enabling a
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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advanced artificial intelligence models (cellular automata, machine learning) of diffusion processes at high temperatures Performing in-depth microscopic and phase analyses of the oxides formation in
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the Defense Innovation Network Assistant (DINA) initiative. The position conducts applied and translational research in machine learning, natural language processing (NLP), retrieval-augmented generation (RAG
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, including gas turbines and combustion engines. We combine advanced computational fluid dynamics, theory, machine learning, high-performance computing, and experimental methods to investigate and model complex
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Infrastructure? No Offer Description Start date/duration as soon as possible until 14.09.2027 Job profile: Your responsibilities: PhD thesis in the field of software engineering and model-driven engineering
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epidemiology, and AI-accelerated drug and therapeutics discovery. DS/AI-driven Natural, Physical, and Earth Sciences, including physics-informed machine learning, climate and environmental modeling, physics and
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of foundational and agentic AI models (LLMs, generative AI, RAG-based architectures) for autonomous, context-aware decision-making. Researching and implementing trustworthy human-machine interaction methods
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Research, design, develop, test, and support artificial intelligence (AI) and machine learning (ML) frameworks and models. Leverage AI/ML techniques to answer business questions, support business strategies
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
but are not limited to: development of new AI architectures for biology and hybrid models that combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and