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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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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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., machine learning, stochastic dynamic programming, simulation). Affinity with (food) supply chain management is preferred. To collaborate with and to co-supervise MSc thesis students and internship students
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model the remarkable learning efficiency of the human visual system. The project is an interdisciplinary collaboration between the the Machine Learning group at CWI in Amsterdam (Prof.dr Sander Bohte) and
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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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modelling of materials and machine learning. Experience in atomistic modelling (molecular dynamics, density functional theory) and machine learning is important, as well as a strong interest in pursuing
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of Amsterdam. Interested in developing fundamental machine learning techniques for tabular data to democratize insights from high-value structured data? Then this fully-funded 4-year PhD position starting Fall
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observations. Your major challenge is in model development, and there is room for you to develop machine learning applications in the field of firn modelling. If successful, your work will lay the foundation
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or showing willingness to learn machine learning models Having interest in stakeholder engagement Desired knowledge of Nature-based Solutions, especially in agriculture and developing country contexts