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groups working towards a common goal. For this postdoc project, we seek a dynamic and motivated candidate with an interest in computational electromagnetism, inverse design, and/or machine learning in
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measurements from real projects, statistically analyse them, and conduct experiments with modern machine learning techniques and generative AI. A strong background in software engineering as well as some
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statistics, unsupervised machine learning, optimisation, model predictive control. Experience in financial mathematics. Having high integrity, be process-oriented and able to work independently. Being able
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emulators for accelerated forward modeling Advanced data-intensive machine learning and AI techniques for survey analysis Applications to major international surveys, including LSST (Rubin Observatory
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: What are efficient machine learning strategies to identify large ensembles of nanoparticles in tomograms (i.e., to identify nanoparticles on irregular 2D surfaces in 3D space)? What are appropriate
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Extensive knowledge of relevant machine learning and AI techniques Self-motivated individual with ability to work independently Teaching and mentorship abilities or interests in personal development A
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, and high-performance computing. About the division and department With more than 30 faculty members, more than 100 PhD students, and more than 300 employees, the Department of Microtechnology and
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consists of 18 research groups covering a wide range of mathematical disciplines – from pure and applied mathematics to numerical analysis and optimization, as well as mathematical statistics and machine
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-from-motion, and object recognition. The main research problems include mathematical theory, algorithms, and machine learning (deep learning) for inverse problems in artificial intelligence, as
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data. Much focus is on large scale analysis based on machine learning, deep learning/AI, as well as handling and analyzing large 3D microscopy data. You will work with shorter and longer projects and