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Field
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wide range of research, including quantum information and simulation, artificial Intelligence and machine Learning, and physics within and beyond the Standard Model at current and future colliders. The
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available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a
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research experience to include: Experience developing novel deep learning methodologies (e.g. transformer models, Convolutional Neural Networks, Auto-encoder models, or LSTM networks). Demonstrable ability
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the Medical College of Wisconsin Cancer Center that is supportive and favorable to learning, and includes opportunities to interact with peers, graduate students, postdoctoral researchers, and other
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geometry, and/or data science. Specific topics of focus include, but are not limited to, linear response, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics
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, train, and validate advanced computational models and machine learning algorithms tailored to complex datasets. Collaborate with multidisciplinary teams including biologists, engineers, and clinicians
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mathematics, biophysics, AI/machine learning, computational biology, computer science/engineering, statistical inference, or related fields are particularly encouraged to apply. POSITION DESCRIPTION Flatiron
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cardiac precision medicine through artificial intelligence and machine learning. The postdoctoral fellow will contribute to the development of a comprehensive, multi-modal framework for predicting and
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We are seeking creative and energetic candidates with strong experience in multimodal machine learning and human behavior analysis and modeling for a one-year Postdoctoral position. Using recent
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journals and conferences such as Journal of Machine Learning Research (JMLR), Transactions on Machine Learning Research (TMLR), NeurIPS, ICML, RLDM, ICLR, IROS, or other top IEEE venues. Experience with