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particular neural networks. A list of members of the statistics group can be found here. The statistics group is embedded within a larger data science initiative at the University of Twente’s department
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methods to be considered for numerical optimization by an Energy and Emission Management System (EEMS). Data-driven AI methods (e.g. Reinforcement Learning and/or Recurrent Neural Networks) to be considered
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position is available immediately in the laboratory of Catherine Marcinkiewcz, Ph.D. at the University of Iowa. Research in the Marcinkiewcz lab focuses on unraveling neural circuit mechanisms underlying
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are unpredictable, e.g., unanticipated changes in the environment of the ACPS may cause a neural network to produce faulty outcomes that could endanger the safety of the system. To assure the safe and reliable
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architectures which leverage our increasing understanding of the behaviour of neural networks trained with DP to ameliorate these trade-offs in biomedical applications. - Foundations of private machine learning
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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, adversarial attacks, and Bayesian neural networks. Excellent analytical, technical, and problem-solving skills Excellent programming skills in Python and PyTorch including fundamental software engineering