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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
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and evolutionary biology or a closely related field. Experience with computational analysis. Experience in assembling and analyzing short-read data (e.g., Illumina) or long-read data (e.g., Oxford