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PhD Position: Physical Computing and Learning in Soft Matter Faculty: Faculty of Science Department: Department of Physics Hours per week: 36 to 40 Application deadline: 24 February 2025 Apply
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simple building blocks can exhibit learning behavior. This fully-funded PhD position offers a unique opportunity to work at the forefront of physics, materials science, and computation. Your job
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change and organizational learning climate i.e., how organizational changes and change mindset develop among team members and affect their ability to adapt. The primary goal of this PhD project is to
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Are you looking for a PhD position where you develop state-of-the-art machine learning methods for the life sciences (geometric deep learning, transformer-based approaches, ...) with a focus on
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for perceptual and creative relevance; Curate and/or utilize benchmark datasets of pareidolic visuals, and apply statistical and machine learning methods to analyze visual data and model behavior; Publish and
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the Department of Machine Learning and Neural Computing (MLNC) at the Donders Centre for Cognition (DCCN) and the School of Artificial Intelligence (AI), both part of the Faculty of Social Sciences of Radboud
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research within the scope of the project culminating in a successful dissertation, as well as writing academic articles and presenting your work on international AI and machine learning conferences
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obtained their PhD between three and seven years ago, and do not hold a position as PI, Associate Professor, Professor or equivalent. An extension arrangement applies to the post-PhD period. Who may submit
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-GUIDE project, we will make directed evolution guidable and, ultimately, predictable by machine learning. Specifically, you will build a first-in-class framework to expedite the design of high-affinity
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of state-of-the-art machine learning potentials for multi-component alloy systems that are relevant for the new green steels compositions, including impurities and tramp elements. These models should enable