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inductive biases, we aim to identify key mechanisms that drive rapid learning in the visual system. The goal is to create a robust mechanistic neural network model of the visual system that not only mimics
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infrastructure data, and building visual tools to support the interpretation and communication of results. The work has a strong foundation in systems and network research, with an emphasis on DNS protocol
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to coordinate and conduct asset management and maintenance activities across infrastructure networks in the Netherlands. Together with our research partners from Next Generation Infrastructure network, the PhD
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for molecular system. Recently, we reported a major breakthrough in using chemical reaction networks for so-called in chemico reservoir computing (Nature, 2024, 631, 549–555). This work demonstrated that self
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. You have a background in machine learning for spatial data (e.g., random forest, neural networks) or are open acquiring these skills. You have experience with handling large geospatial datasets and
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of the NWO funded EMBRACER project external link , which provides a large network to get acquainted with the current challenges in climate research and the modelling techniques in climate science. Your
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environments. Additionally, you will drive the network and knowledge-sharing among REFM colleagues, who also work on workplace development, across our sites. About the REFM Department: The Real Estate and
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. The University of Groningen is an international centre of knowledge: It belongs to the best research universities in Europe and is allied with prestigious partner universities and networks worldwide. The Faculty
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contribute to smart grids that make energy networks more efficient, mathematical models that assist medical doctors, schedules that make hospitals more efficient and numerical schemes to study multiscale fluid
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your professional network. You will also have opportunities to present your work at leading conferences, engage in international collaborations, and develop a strong professional profile in computational