58 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at The University of Arizona
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Qualifications PhD in Robotics, Electrical & Computer Engineering, Mechanical Engineering, Biomedical Engineering, Computer Science, or closely related field. Must have PhD conferred upon hire. Preferred
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Sign In Create Profile Postdoctoral Research Associate, Electrical and Computer Engineering Tucson, AZ, United States | req24106 Apply Now Share Save Job Posted on: 10/6/2025 Back to Search
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Experience with super-resolution ultrasound, US localization microscopy, photoacoustic imaging, elasticity imaging, pulse encoding, solving inverse problems, machine learning, AI, SolidWorks, 3D printing FLSA
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any of the following areas is desirable Collection and analysis of dense nodal seismic datasets Numerical simulations of the seismic wavefield High-performance computing Machine learning applications in
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to the publication. Contribute (writing and figures) to grant submissions related to this project. Train lab members in various computational techniques. Minimum Qualifications PhD in Neuroscience or related field
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for manuscripts related to the publication. Contribute (writing and figures) to grant submissions related to this project. Train lab members in various computational techniques. Minimum Qualifications PhD in
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Postdoctoral Research Associate to join our research group working at the intersection of advanced manufacturing, computational modeling, and machine learning. This position offers an opportunity to contribute
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evaluation of alloys; familiarity with finite element methods. Skills and experience in programming, machine learning, or signal processing are all considered a plus. Outstanding UA benefits include health
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-generation computing, including quantum emitters and neuromorphic transistors. The ideal candidate should have a strong background in solid-state physics, electronic materials, or device fabrication and
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on semiconductor devices for next-generation computing, including quantum emitters and neuromorphic transistors. The ideal candidate should have a strong background in solid-state physics, electronic materials