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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 hour ago
inform observable macroscopic properties as part of the activities of the UNC Superfund Research Program (SRP) (https://sph.unc.edu/superfund-pages/srp/). This work involves running, developing, and
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topics in international macroeconomics and finance Responsibilities • Conduct empirical analysis • Review and summarize academic literature • Assistant in theoretical and computational modeling • Prepare
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, embedded system implementation, and end-to-end signal analysis workflows, positioning them at the forefront of AI-driven research in radiofrequency spectral monitoring. Duties The work will mainly include
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of Engineering Office of Academic & Student Affairs. Provide comprehensive reports and data analysis for departmental, university, and external reviews, and develop and maintain databases to track student academic
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to attract a pool of qualified people who are available to assist in online courses in our Master of Science degree program in Crime Analysis. Academic Associate positions are fixed-term appointments
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NEST: https://nest-simulator.readthedocs.io Your tasks in detail: Work with the NEST main code base and experimental branches Dissect the spiking network simulation cycle into phases and capture the flow
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Overview Do you have a background in molecular biology and an interest in applying your technical skills to real-world challenges? We are seeking a Research Assistant to support the analysis
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Methods Reachability Analysis Computational Geometry Context The applicant will be directly advised by Prof. Matthias Althoff ( https://www.ce.cit.tum.de/cps/members/prof-dr-ing-matthias-althoff/ ). Besides
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of these labs and brings together researchers from the Department of Health Science and Technology (Faculty of Medicine) and the Department of Mathematical Sciences (Faculty of Engineering and Science). The
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of 3D crystalline structures; – depending on the candidate's profile, implementing machine learning methods (AI & machine learning) for the analysis of physicochemical data from the hpmat.org database