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reinforcement learning algorithms and contribute to the joint development of the broader modelling and policy framework. Your work will focus on multi-criteria reinforcement learning, uncertainty-aware decision
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management systems (BMS). Ability to develop and implement algorithms for modelling, estimation, or control applications. Strong analytical thinking, problem-solving ability, and capability to conduct
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); Optical system analysis and simulation; Developing AI/ML algorithms; Perceptual and objective visual quality assessment for holograms. For this function, our Brussels Humanities, Sciences & Engineering
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reinforcement learning methods can be used to solve multiobjective discrete and combinatorial optimization problems. The goal is to develop new algorithmic approaches that combine ideas from machine learning
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through theory and simulation and/or experimental design and testing; developing new image reconstruction algorithms for providing more information with less radiation; and applying our techniques
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. Project Description: The project will be carried out in the research group of Prof. Dr. Frank Ortmann at the Technical University of Munich (TUM). Our research focuses on the development of efficient
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microscopy is often limited by slow acquisition speeds and large volumes of redundant data, restricting its applicability in real-world scenarios. The goal of this PhD project is to develop a snapshot spatio
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experimental settings. In addition to fieldwork, the PhD candidate will contribute to the development of novel inversion algorithms for EMI and GPR based on full-waveform inversion techniques. These methods aim
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The Fraunhofer Institute for Algorithms and Scientific Computing SCAI in Sankt Augustin, near Bonn, has around 180 employees who research and develop innovative methods in the field of computational
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PhD working with Prof. Hendrik Blockeel and/or Prof. Jesse Davis on analysis of time series data. The goal is to develop algorithms for detecting anomalies, discovering “motifs” (repeating patterns