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computational models with the "exact" but lower resolution information available from experiments. Job description: - Research and teaching is done on statistical physics and machine learning in physics
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systems using various tools and models, including: i) characterization of the emerging patterns in physical systems (solid state materials and active systems); ii) investigation of the mechanical properties
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architectures, capable of capturing the structure of complex, high-resolution NMR spectra – analogous to how language models such as ChatGPT learn the structure of human language. One of the primary goals is to
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, C/C++ and/or Java, etc.; experience with the implementation of specialized transport modelling software, optimization algorithms and procedures; strong ability and desire to learn new programming
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of machine learning and health sciences, with unique access to experimental and clinical data. Embedded in Munich’s thriving AI landscape, fellows benefit from world-class facilities, interdisciplinary
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. Since 2020, Fraunhofer Heinrich Hertz Institute has worked with United Nations
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Your Job: As part of an interdisciplinary team, you will develop approaches for the automated and large-scale provision and integration of energy systems data and models and apply data science
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analysis of large data sets, statistical modeling, and knowledge of at least one programming language (e. g.: R, Python and/or Julia) are required. Experience in machine learning and image recognition
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of computer science or using computer vision methods Excellent knowledge of the development and implementation of methods in the field of digitization, artificial intelligence, machine learning and/or 2D/3D imaging and
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multimodal datasets Design and fine-tune machine learning and deep learning models to extract meaningful patterns and predict metastatic behavior Collaborate closely with experimentalists for mechanistic