694 machine-learning-"https:"-"https:"-"https:"-"Linnaeus-University" Postdoctoral positions in United States
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), supernovae, gravitational wave sources, magnetic activity in exoplanets, and machine learning classification. The candidate will lead and participate in observations and data analysis of these sources in all
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related field • Strong quantitative background (e.g. ecological theory and mathematical modeling, hierarchical statistical modeling, machine learning, remote sensing, geospatial statistics) • Demonstrated
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computational physics, computational materials, and machine learning and artificial intelligence, using the DOE’s leadership class computing facilities. This position will utilize methods such as finite elements
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Geosciences, Environmental sciences, Civil or Environmental Engineering, Physics or Mathematics or a related discipline Experience in programming (e.g., Python, MATLAB, or similar), interest in machine learning
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working on and publishing refereed papers on problems related to discrete and continuous optimization and machine learning is preferred A combination of education and relevant experience from which
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We are seeking a highly motivated Postdoctoral Appointee with a strong background in artificial intelligence and machine learning (AI/ML), with particular emphasis on the development and application
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. Candidates whose doctoral work focused on deep learning methods and who have a strong interest in genomics will also be considered. Experience: At least one publication in computational genomics or machine
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 6 hours ago
, biophysics, or in a related interdisciplinary field. Experience with AI and machine learning tools is preferred. Applicant instructions: Applicants should be prepared to upload the following documents when
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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working on and publishing refereed papers on problems related to discrete and continuous optimization and machine learning is preferred A combination of education and relevant experience from which