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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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Vacancies 2 PhD Positions to contribute to a project crafting the future of healthcare decision making Key takeaways In the section Health Technology and Services research at the University
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that activity-silent mechanisms, such as short-term synaptic plasticity, also play an important role. We will experimentally target these two mechanisms, using EEG in combination with machine learning to reveal
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Apply now The Faculty of Science and the Leiden Institute of Advanced Computer Science (LIACS) are looking for a: PhD Candidate, Secure Computation Technologies and Applications to Machine Learning
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weather prediction using Machine Learning approach (hybrid forecast). The app is also expected to be equipped with seasonal forecast for agricultural planning. You will co-design the short-, medium-, and
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, Industrial Engineering, Computer Science, or Machine Learning. Solid experience with quantitative optimization methods, including (but not limited to) mathematical programming and stochastic dynamic
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of the processing system online. Our approach will be to draw on a broad selection of tools including (deep) reinforcement learning, queuing networks, online algorithms and systems engineering. In addition, a large
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faces. At the same time, reinforcement learning is a key technology in artificial intelligence and machine learning that set various state-of-the-art results. In the Reinforcement Learning Lab
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UC Berkeley after obtaining her PhD from the University of Amsterdam for which she did research at MIT and Sigma Computing. Her general research interest is on the intersection of machine learning