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Field
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Essentials PhD (completed or near completion) in Computer Science, Computer Vision, NLP, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative
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Essentials PhD (completed or near completion) in Computer Science, Computer Vision, NLP, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative
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] Subject Areas: Physics / Hard Condensed Matter Theory , Machine Learning , Material Science , Physics , Quantum Information Science , Soft Condensed Matter Theory , theoretical condensed matter physics
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/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines for plausible narratives of regional climate change, novel algorithms for rare
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wide range of resources and is mostly not publicly available. While sharing proprietary data to train machine learning models is not an option, training models on multiple distributed data sources
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machine learning and statistics; experience with Gaussian process regression and/or probabilistic regression. Experience with normative modelling is an advantage. Proficiency in Python (and ideally C/C
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of Biostatistics at University of Florida and Dr. Hongkai Ji (remote) in the Department of Biostatistics at John Hopkins University. This position, available immediately, focuses on developing statistical, machine
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research associate positions broadly in statistics and machine learning with Prof. Jason M. Klusowski (https://klusowski.princeton.edu ). The position is for one year with the possibility of reappointment
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or commensurate with experience Type of Position Student Position Time Status Full-Time Required Education PhD or MD Required Related Experience Biomedical Sciences Required License/Registration/Certification NA
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analysed by bespoke machine-learning driven algorithms, combined with physical models, to de-noise images, identify features and correlate properties, giving critical insights into power loss pathways