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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 21 hours ago
collaborate with colleagues from multiple universities across the Research Triangle, the United States, and even the world. Position Summary The Research Assistant (RA) will support advanced research in
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applications to fill multiple faculty positions in the field of quantum information science, broadly defined. Faculty will join the newly established Institute for Quantum and Information Sciences
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applications to fill multiple faculty positions in the field of quantum information science, broadly defined. Faculty will join the newly established Institute for Quantum and Information Sciences
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well as in the design of machine learning algorithms (ANN, SVM, Decision Tree, and Random Forest) applied to healthcare, will be particularly valued. Proficiency in programming tools (Matlab) and statistical
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Group , a leader in innovative multi-sensor atmospheric remote sensing from ground, airborne, and satellite platforms. Our group develops advanced algorithms and data analysis methods to address
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learning algorithms into professional software with an intuitive user interface, incorporating feedback from CHWs through iterative design and evaluation cycles. The selected candidate will be part of a
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to the body of knowledge by enhancing the safety, relia- bility, and efficiency of automated vehicles by developing a collaborative multimodal perception system. This system leverages data from multiple sources
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2 Sep 2025 Job Information Organisation/Company CNRS Department Maison de la Simulation Research Field Computer science Mathematics » Algorithms Researcher Profile Recognised Researcher (R2) Country
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multi-sensor atmospheric remote sensing from ground, airborne, and satellite platforms. Our group develops advanced algorithms and data analysis methods to address fundamental scientific challenges
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for Multi-Agent Decision-Making, https://oceanerc.com ). This timely project will develop statistical and algorithmic foundations for systems involving multiple incentive-driven learning and decision-making