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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 12 days ago
development of data processing tools. The position is for one year with the possibility of reappointment for an additional year. It will involve developing, implementing, and validating novel algorithms
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algorithm, and basic understanding of diagrams are required. Special Instructions to Applicant Please upload resume online. Quick Link for Direct Access https://ecu.peopleadmin.com/postings/88404 Open Date 08
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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at the intersection of computation and life sciences. We are seeking a Research Associate. The position will perform statistical modeling of single-cell multiome data; development of algorithms to link enhancers with
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troubleshooting to ensure real-world readiness. Develop and implement algorithms for autonomous control, SLAM and navigation. Ensure reliable communication between hardware components and the robotic system
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Health. Complex Procedure Scheduling Duties Determine if ancillary appointments (pre-procedure clinic evaluations, pre-anesthesia testing appointments, etc.) are needed according to scheduling algorithms
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according to scheduling algorithms, patients' medical history and established clinical and facility standards. Coordinate both procedures and ancillary visits within a specific period established for each
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in histology and molecular biology techniques. Experience with IACUC and institutional protocols. Experience with transcriptomic bioinformatics analysis (Seurat, Scenic algorithms on 10x Genomics
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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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Science and Engineering, or a related area is required. The position will involve developing models and algorithms for the evolution of inorganic aerosols in the atmosphere, building upon the research