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the following areas is desirable: collection and analysis of dense nodal seismic datasets, ambient noise tomography, high-performance computing, machine learning applications in observational
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Genesis project at Biosphere 2. Develop new approaches using machine learning and AI to improve inversion, interpretation, and autonomous monitoring. Publish peer-reviewed research and contribute to
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Mathjobs.org | about 1 month ago
applications are particularly encouraged to apply. Expertise in statistical or machine-learning methods is also welcome, as connection with experimental and clinical data is important for our work. Prior
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applications are particularly encouraged to apply. Expertise in statistical or machine-learning methods is also welcome, as connection with experimental and clinical data is important for our work. Prior
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Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a closely related technical field. Demonstrated knowledge of or interest in Indigenous Knowledge Systems and
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Postdoctoral Research Associate, Adaptive Learning Agents and Computational Cognitive Science Posting Number req27365 Department Psychology Department Website Link https://psychology.arizona.edu/ Location
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, astrophysical, and gravitational-wave observables; development of statistical-inference and machine-learning methods; direct analysis of current and future surveys or experiments. As members of the Arizona
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, machine learning, and statistical expertise. The position is funded by a grant from the NIH / NIAID (R01AI135114), though opportunities for additional projects in other areas are available according to
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related languages • Experience with image processing, registration, machine learning, or computational neuroscience methods • Interest in aging, neurodegenerative disease, developmental disabilities, or
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Qualifications* - PhD in Mechanical Engineering, Robotics, Computer Science, Electrical Engineering, or a related field - Strong background in computer vision and/or machine learning, with hands-on