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in data integration, model design, and large-scale training by combining multi-modal scientific data, knowledge graphs, physics-aware machine learning, and GPU/HPC computing to develop transparent and
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. About You You will have, or be close to completion of a PhD/DPhil in Statistics, Machine Learning, Data Science, or a related quantitative discipline. You will demonstrate strong specialist knowledge in
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electrophysiology data obtained through collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in
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participant outcomes. The project will use a variety of approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant
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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness
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interdisciplinary team spanning statistics, machine learning, genetics, and population health. You will work closely with collaborators at the Nuffield Department of Population Health (NDPH), the Big Data Institute
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methodologically strong and motivated to work at the intersection of applied machine learning, social sciences, and natural sciences. Essential qualifications: A completed PhD in data science, computer
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year. You should have knowledge and experience in bridging quantum and classical machine learning, and be fluent in English, both written and spoken. Assesment criteria Qualifications that are considered
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at the intersection of educational data science, AI in education, and the learning sciences, with additional advisory support from faculty and researchers across learning sciences, computer science, machine learning
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour ago
missions (e.g., Surface Biology and Geology - SBG). This could involve advancing atmospheric correction, dimensionality reduction, or machine learning approaches for handling big data in order to improve