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Field
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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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communication skills are encouraged to apply. A MSc degree (or equivalent) in Mechanical Engineering, Computational Physics, Materials Science or a related discipline is required, with experience in atomistic
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The successful candidate should have: Master’s degree in Industrial Engineering and Management, Mechanical Engineering, Energy Systems, Control Engineering, Data Science, or a related field, with solid foundations
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expected to uncover the vital mechanism of selective IDP-mediated transport and thus answer one of the central questions in molecular cell biology. We collaborate with experts in cell biology and MD
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dissecting RNA structure ensembles in living cells. Particularly, you will: Develop novel methods (both wet and computational) for the accurate interrogation of RNA 2D and 3D structures in living cells. Apply
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diverse range of nationalities, and an active visitor program which attracts the best string theorists from all over the world. Our group's faculty members include: Jay Armas, Daniel Baumann, Jan de Boer
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will explore how institutions — formal rules, informal norms, and governance mechanisms — can better support the maintenance, adaptation, and transformation of public space. You’ll study how
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mobilized to drive new value chains, and how impact investing and true pricing mechanisms can support the growth of circular business models. Your work contributes directly to the development of a future
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structured data that prevails in governments, humanitarian organizations, enterprises, and healthcare. The anticipated research will focus on developing mechanisms for reliable and responsible AI-powered data
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the developmental stages of human infants. By comprehensively measuring and documenting these developmental stages and the associated inductive biases, we aim to identify key mechanisms that drive rapid learning in