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Field
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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about applying deep learning to decode the regulatory grammar of plant genomes and translating predictions into testable biological hypotheses, we invite you to join the Omics Data Analysis and
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characterising chronic diseases and disease patterns from electronic health record (EHR) data through the development of advanced deep learning methodologies based on state-of-the-art foundation models. You will
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on machine learning, deep learning, control systems, sensing systems, and related technologies. Supports the development, testing, and transition of emerging autonomous technologies through laboratory
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, and industry research programs. This is an opportunity to build deep scientific expertise while developing skills relevant to careers in academia, biotechnology, genomics, diagnostics, and clinical
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genetic basis of plant–microbe interactions, with a particular emphasis on data integration across plant species and data types (genomics, transcriptomics). Design, adapt and use deep learning methods
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University of Massachusetts Chan Medical School | Worcester, Massachusetts | United States | about 2 months ago
Minimum: USD $62,232.00/Yr. Salary Maximum: USD $75,564.00/Yr. GENERAL SUMMARY OF POSITION: Postdoctoral positions in Deep-Learning Omics are available in the Zhou Lab (https://profiles.umassmed.edu/display
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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related field; Strong publication record in relevant venues; Solid programming skills in Python and a modern deep-learning framework Excellent communication skills in spoken and written English; Self
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boson decays, searches for supersymmetry and other new phenomena, and measurements of rare standard model processes. We vigorously pursue the use of machine learning techniques for data analysis