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, specifically methods that combine machine learning and optimization with physics-based simulation and/or physical constraints and translate these methods into impactful industrial applications. The position is
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, medical informatics, databases, data mining, machine learning, applied mathematics, biomedical modelling and analysis of complex networks. Joint data science projects between the different partners
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning
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Learning at TUM works on machine learning, artificial intelligence, and information processing, with a current focus on foundation models, data-centric research, and applications to scientific and medical
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challenges, the school provides a wide variety of topics, from logic in autonomous cyber-physical systems to machine learning in Earth System models. You will have one supervisor from the mathematical sciences
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of AI-supported coding assistance functions (e.g., suggestion models, highlighting of relevant text passages, active learning strategies) to accelerate coding while ensuring high quality. Systematic
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). These collaborations enable practically relevant and breakthrough results. This team goal requires a quantitative model describing and predicting sperm motility under various conditions. You will develop the digital
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quantitative focus on these fields Solid foundation in statistics and/or machine learning, e.g., supervised learning, regression modeling, model evaluation, or high-dimensional data analysis Good programming
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take place monthly. A lecture series on theoretical and experimental neuroscience as well as machine learning is addressed primarily to doctoral students. Lectures are held by principal investigators