10 condition-monitoring-machine-learning Postdoctoral positions at University of Houston Central Campus
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working at the intersection of computer vision, and 3D AI for the built environment. Our goal is to transform how physical environments and infrastructure are captured, analyzed, and monitored using
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strong interest in machine learning and deep learning algorithms · Research and publication experience in health outcomes research Application Materials: · CV · Cover letter · Contact information for three
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qualified candidates to apply for a postdoctoral research position. The research will focus on statistical modeling, machine learning and trustworthy AI to develop effective and efficient algorithms
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members across electrical engineering, earth science, signal processing, and machine learning to ensure methodological rigor and practical relevance. Qualifications: -Ph.D. in Electrical Engineering
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experiences related to one or more of the following areas: embedded or mobile device security, embedded systems applications, machine learning security, user-centric security and privacy. 3. Experience and
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focuses on the study of magnetic and electronic excitations in new materials such as unconventional superconductors, 2D magnets. We also studies these materials under operating conditions and under laser
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. The research interests include superconductivity, topological physics, fundamental condensed matter physics at extreme conditions, high static pressure technology, and development of novel diagnostic
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support the CPP research, grant applications, and outreach. During the academic year the fellow will teach one or two courses. Position Qualifications: Required: PhD in Public Policy, Political Science
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papers to be published with the faculty supervisor. In addition, the applicant will teach one course per year at the undergraduate or postgraduate level and will participate and contribute to college
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skills to allow for completion of assignments that cross fields of specialization. Develops leadership and management skills. The Texas Center for Learning Disorders (TCLD), in collaboration with