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Field
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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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of the following areas is an advantage: Modelling and simulations of flow in porous media. Programming, image processing and machine learning Personal and relational qualities will be emphasized. Motivation
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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
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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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, 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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(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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with machine learning techniques for robotic decision-making and intelligent control for tasks with high uncertainties. Experience with research on multi-agent collaboration and decentralized control
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strong, demonstrated interest to conduct academic research in a relevant field Interest in legal research Interest in causal inference and social science Experience with machine learning / deep learning
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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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machine learning Personal and relational qualities will be emphasized. Motivation, ambitions and potential will also count when evaluating the candidates. Special requirements for the position