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programme. To acquire, analyse, interpret and evaluate research findings/data using approaches, techniques, models and methods selected or developed for the purpose. To establish a national reputation and
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time role, 0.1FTE. The activities of this role will support development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning
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postdoctoral fellow, committed to advancing inclusive and interdisciplinary science, to join an international team applying state-of-the-art machine learning technologies to stem cell and immune engineering in
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Learning Research (JMLR), Transactions in Machine Learning Research (TMLR), and IEEE Big Data. Methodological areas of particular interest to our laboratory include (1) probabilistic modeling
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learning techniques to enhance the predictive model for protein adsorption. Collaborate effectively with cross-functional teams comprising experts in AI/ML, computational modeling, structural biology
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foundation models and agentic systems and demonstrated capability to produce workable solutions from theoretical formulations Substantial publication history in top computer vision/machine learning venues
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(particularly under extreme conditions), and/or the use of machine learning for solid mechanics/stress analysis problems are encouraged to apply. The job description presented here is deliberately broad due
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the software quality certification/robustness to cyber disturbances, cyber-attacks, on federated/distributed EC environment. Identify quality issues with AI models implementation on edge and on offering
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, committed to advancing inclusive and interdisciplinary science, to join an international team applying state-of-the-art machine learning technologies to stem cell and immune engineering in the Zandstra Stem
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well as in a collaborative, team-oriented environment. Preferred Qualifications: Prior experience working with mouse models of cancer is strongly preferred; candidates without prior experience will be