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of state-of-the-art machine learning potentials for multi-component alloy systems that are relevant for the new green steels compositions, including impurities and tramp elements. These models should enable
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will gain the training and experience necessary to conduct independent research through coursework in information systems, economics, econometrics, machine learning, and large-scale data analytics. You
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that would give you an advantage) Experience in computational modelling (e.g., agent-based Bayesian models, cognitive learning models, machine learning, robotics). Experience in annotation software such as
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mechanics at the atomic scale. In this project, the University of Groningen will develop an array of state-of-the-art machine learning potentials for multi-component alloy systems that are relevant
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adaptation of state-of-the art machine learning codes to deal with redshift distortions, intrinsic (galaxy) biases, survey selection biases and in particular the complications encountered in photometric
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intelligence, or a related discipline. You have a strong interest in performing foundational algorithmics and machine learning research. You have mature mathematical knowledge and programming skills (e.g
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investigate how machine-learning based algorithms can be used to personalize the user experience. The goal of this personalized user experience is to enable each individual user to discover their own
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. Legal systems worldwide—and particularly within the European Union (EU)—are facing urgent challenges in addressing the ethical and societal impacts of AI-driven applications and machine-learning
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learning approaches and contributing to the development of better (bio)catalysts and drugs? We are offering three fully-funded, 4-year PhD positions at the University of Groningen or the Technical University
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(+/–) applied to the electrodes can be optimised in a machine-learning approach to optimise the gel morphology and the embedded stimuli-transduction pathways towards the targeted mechanical behaviour. Departing