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the project. What you will do Conduct original and high-quality research in machine learning and computer vision; Develop novel algorithms for adapting and specialising visual foundation models; Publish
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and experiments with modelling of these experiments. As part of this, we have developed new algorithms and a completely new web-based platform – EasyNMR - for performing such modelling/simulations. With
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was created to advance research in the mathematical, algorithmic, and statistical foundations of data science and their application to other disciplines. In addition to providing support to core foundational
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; Develop novel algorithms for adapting and specialising visual foundation models; Publish research findings at leading machine learning and computer vision venues such as CVPR, ICCV, ECCV, NeurIPS, and ICLR
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around 250 of these in full-time positions. In addition, there are more than 100 postdocs and more than 400 PhD students. As a postdoc, you will be affiliated with the Department of Clinical Medicine, but
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data analytics, and manufacturing process optimization. Develop and apply models, algorithms, or data analysis workflows to support machining process understanding, machine tool characterization, process
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, optimal transport, set evolution and shape optimization, calculus of variations), and/or convex/non-convex optimization methods (first-order algorithms, nonlinear/non-Euclidean gradient descents
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demonstrations. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development. Responsibilities will include programming
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for Advanced Computing. Essential Qualifications PhD in computer science, physics, chemistry, materials science, or a related field is required by the start of the appointment Experience with programming
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are especially encouraged: (1) statistical mechanics, condensed-matter theory, quantum field theory; (2) tensor networks, quantum information, quantum algorithms; (3) machine learning, generative models, large