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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
Engineering stresses the application of science and engineering, mathematical analysis, and computer techniques to biomedical problems. The research and entrepreneurship of the faculty, students, and staff aim
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closely related field at the time of appointment. Preferred qualifications ● Domain knowledge in energy studies, power systems, renewable energy grid integration, and optimization models. ● Extensive
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& Technology, University of Florida, is seeking a highly motivated postdoctoral associate. The lab investigates the neural mechanisms underlying reinforcement learning and cognitive processes such as decision
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The University of Nevada, Reno (UNR) appreciates your interest in employment at our growing institution. We want your application process to go smoothly and quickly. Final applications must be
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: Define the critical engineering requirements (optical pulse duration, terahertz crystal materials in cryogenetic environments, signal-to-noise ratio optimization, entanglement witnesses and verification
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optimization, and application-driven performance analysis for HPC, scientific Artificial Intelligence (AI), and scientific edge computing. We are a leader in computational and computer science, with signature
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meetings. Propose, plan, and conduct laboratory-based experiments - 60% • In collaboration with the PI and other lab personnel, propose, optimize, and plan experiments that will advance our lab’s research
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, particularly R01EY033829 (Optimizing genetic risk models for primary open-angle glaucoma through expanded population genomic analysis). Duties include: hypothesis development, literature review, collection
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properties from telescope data. The design of robust uncertainty quantification tools is a core component of this effort. -On the experiment design side, the group develops simulation and optimization
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compliance, reproducibility, and interoperability across scientific domains. By improving data readiness processes, this role will amplify the potential of AI-driven discovery in areas such as high energy