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Field
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machine learning, reduced-order modeling, or data-driven modeling of physical systems. You will conduct research on the development of fast, trustworthy, and data-efficient surrogate models that
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autonomous, closed-loop (“self-driving”) laboratory workflows. The role integrates catalyst synthesis, high-throughput reactor testing, and in situ/operando characterization with machine-learning and
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Postdoc position in AI and Machine Learning for Electromobility Reference number REF 2026-0337 Do you want to contribute to the future of AI-driven electric transport systems? Join our research group
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scholar will develop statistical machine learning and artificial intelligence methods (ML/AI) for diverse biological data in relation to cardiovascular and neurodegenerative diseases. The specific areas
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analysis in the Wasserstein space of stochastic processes (contact Johannes Wiesel [email protected] ) Causal modelling and related questions in reliable machine learning (contact Sebastian Weichwald
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recent advances in genetics and genomics, and through collaborations with groups using machine learning. We are developing and applying tools to understand how implicated genes act in neurons and
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affiliations. Be You. Be Bold. Choose Duke. Be You. At Duke, we celebrate individuality and the unique perspectives that each member of our community brings. As the Machine Learning Research Data Analyst
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, machine learning, computer science, statistics, engineering, medicine, or a related field. Strong candidates may have experience with large-scale human datasets, machine learning, statistical genetics
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, big data frameworks, bioinformatics, data analysis, data science, discrete and machine learning algorithms, distributed, intelligent, and interactive systems, networks, security, and software and
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-edge research in spatial and temporal data analysis using advanced machine learning technologies. The successful candidate will become a part of an interdisciplinary team working to develop machine