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element methods. Additionally, expertise in computational fluid dynamics, solid mechanics and fluid-solid interaction is essential. Knowledge or experience in multi-scale modelling and machine learning
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to science. This is the first large-scale study of its kind, and your results will establish a legacy of scientists working with funding councils to defend their research. Cutting-edge machine learning
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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: A PhD in Computer Science, Engineering, Mathematics, theoretical Physics or other degree programs from top universities involving at least one of the following topics: Machine Learning, AI, Dynamic
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training and guidance to junior undergraduate and graduate students. Education: A PhD in Neuroscience, Computational Neuroscience, Machine Learning in image analysis, or a related field, with significant
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within the Clusters of Excellence ‘Machine Learning for Science’ and ‘Image-Guided and Functionally Instructed Tumor Therapies (iFIT)’. Requirements PhD in Bioinformatics, Computational Biology, or a
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have the opportunity to develop independent research aligned with the aims of the ADN lab. Current work focuses on machine learning and multivariate decoding of neuroimaging data to predict subjective
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: A PhD in Computer Science, Engineering, Mathematics, theoretical Physics or other degree programs from top universities involving at least one of the following topics: Machine Learning, AI, Dynamic
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We are seeking a Postdoctoral Researcher in Human-AI interaction to join a research group focused on studying learning and decision-making in humans and machine learning systems led by Prof Chris
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also have or be close to completing a PhD in any of the following areas as well as the will and commitment to learn relevant topics from the other areas: Statistical and machine learning, mathematical