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Field
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processes that produce energy and raw materials. The Department of Thermodynamics of Actinides is looking for a PhD Student (f/m/d) - Machine Learning for Modelling Complex Geochemical Systems. The job
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Bayesian machine learning to improve risk management for bridge portfolios. We offer a funded PhD position in an excellent research environment. The project Our infrastructure is aging, and decisions about
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machine learning approaches. These are similar to earlier work on charge and excitation energy transfer (see https://constructor.university/comp_phys). The project for the PhD fellowship is slightly more
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and others) Analysis of the experimental data, ideally connecting to our machine learning tools Presentation of scientific results on conferences and in publications Requirements PhD degree in physics
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to the position must hold a doctoral degree in social or behavioral sciences (incl. human-computer interactions with relevant experience). Applicants must demonstrate experience in experimental work with human
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-party research funding are expected. We are particularly interested in a candidate in any field of economics who leverages state-of-the-art machine learning and causal inference methods to innovative
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of distributed computing, machine learning, image and text analysis, randomized data structures, high-performance computing, and quantum algorithms. Beyond this research, we aim to support computational thinking
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machine learning approaches to quantitatively analyze experimental data and predict emergent multicellular behaviors under varying mechanical and chemical environments. For more information about our lab
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, storage, accessibility/sharing, archiving, publication, and preparing data for machine learning applications. The Research Training Group RTG 3120 offers, subject to the availability of resources, a
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, storage, accessibility/sharing, archiving, publication, and preparing data for machine learning applications. The Research Training Group RTG 3120 offers, subject to the availability of resources, a