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, imaging). • Solid foundations in signal processing and statistics. • Experience with machine learning for regression (e.g., tree-based methods, neural networks) • Hands-on experimental skills: ability and
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radiation beamline hands-on experience in X?ray microscopy techniques, X?ray diffraction or X?ray fluorescence working knowledge of image processing e.g. using machine learning German skills For further
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and imaging diagnostics. The degree of resolution – already at single-cell level – will continue to increase in all disciplines of life sciences, and the need to process and combine huge data sets
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programming languages such as Python and experience with deep learning frameworks (e.g., PyTorch, TensorFlow) is highly desirable strong interest in interdisciplinary research combining imaging, machine
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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science and information science techniques. Several areas of computer science and mathematics play important roles: data management and engineering, machine learning and data analytics, signal and image
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12.01.2026, Academic staff The Professorship of Machine Learning at the Department of Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13 100%; initial contract 1.5
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on developing the imaging system as well as novel machine learning approaches for image analysis and disease classification using field data from German and Brazilian agricultural trials. Responsibilities Design
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experience in scientific computing and software development; familiarity with C++ and Linux environments is an advantage Strong background in deep learning for image analysis / computer vision, ideally
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with