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cutting-edge analytical approaches (Multilevel Vector Autoregressive Models, Dynamic Structural Equation Modelling, Hidden Markov Models, Causal discovery algorithms, Reinforcement Learning), Contributing
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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI
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to join our cutting-edge team, working on the development of advanced AI/ML algorithms for battery management systems (BMS) in electric mobility and micro mobility applications. The primary focus will be
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algorithms Predicting structured output Self-supervised learning Computational metabolomics Computational biomedicine Computational drug discovery Excellent technical and communications skills are required
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wafers are processed across hundreds or even thousands of manufacturing tools following highly complex workflows. We aim to develop optimization models and algorithms to improve wafer processing sequences
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, Atmospheric and Oceanic Sciences, Geosciences, Computational Science and Engineering, or a related area is required.The position will involve developing models and algorithms for the evolution of inorganic
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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis
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) Experience in deep learning algorithms is a plus Ability to work in a highly international team and interdisciplinary project applicants are expected to have excellent language skills in English Opportunity
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At the Faculty of Engineering and Science, Department of Materials and Production one or more Postdoc positions in the area of Optimization and Algorithm Design are open for appointment from April
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, the successful candidate will conduct cutting-edge research in: Adaptive incentive mechanism design for complex sociotechnical systems Strategic learning and equilibrium-seeking algorithms in transportation