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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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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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Science Statistics / Biostatistics Applied Mathematics Data Science Demonstrated expertise in modern machine learning, including at least one of the following: Deep learning (e.g., transformers, sequence models
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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for early-stage cancer using statistics and/or machine learning (including deep learning where appropriate). You will join a vibrant and growing research group of 12 scientists (six postdoctoral researchers
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Project Overview We are hiring highly motivated and talented Postdoctoral Associates who are interested in advancing the state of the art in resource-efficient machine learning at the Singapore-MIT
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original research on clandestine printing networks using computational tools Contribute to publications in both AI and humanities venues (machine learning conferences and book history journals) Contribute
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are looking for a researcher with a PhD (or near completion) in engineering, data science, computational social science or a related discipline, with experience in data analytics, NLP or machine learning. You
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$54,000 - commensurate with experience Type of Position Temporary Position Time Status Full-Time Required Education PhD or terminal degree in a related field Required Related Experience Experience
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(SHORES) and the Division of Engineering, New York University Abu Dhabi, seek to recruit a Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital