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Location ST. LOUIS, MO 63110 Scheduled Hours 40 Position Summary The Carter Lab investigates how evolutionary changes in our DNA gave rise to uniquely human biology. We combine human and ape stem
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of results and preparation of high-quality scientific publications within an interdisciplinary project combining forest genomics, evolutionary ecology and forest disturbance ecology. Applicants should have: a
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The EvOmics group at Aarhus University is seeking a postdoctoral researcher to join an interdisciplinary project that combines plant genomics, artificial intelligence, and evolutionary biology. The
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on the mathematical theory of deep learning as part of the joint NSF-EPSRC project “DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
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University of Massachusetts Chan Medical School | Worcester, Massachusetts | United States | 2 months ago
biology as a high-dimensional, dynamic, networked system, applying techniques from machine learning, causal inference, statistics, and algorithms. No prior biomedical training is required—just strong
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machine learning, deep learning, audio/image/video classification, attention mechanisms, zero/few shot learning, and evolutionary algorithms. The Research Associate should have proficient programming skills
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analysis, machine learning, mathematics of data scienceMathematical modeling and manifold learning of high-dimensional data geometry, with theoretical foundations and applications in evolutionary biology and
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advance the fundamental science of artificial intelligence and address some of the field's most important challenges. You will be responsible for researching and developing novel algorithms and techniques
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NSF-EPSRC project “DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks”. The research will involve collaboration with
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study at the forefront of chronic pain epidemiology, helping to validate an algorithm for identifying neuropathic pain using linked healthcare data. Your work will generate evidence on prescribing