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Field
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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/Licenses Required Knowledge, Skills, and Abilities Experience in metabolomics and flow cytometry (FACS) analyses, or the ability to rapidly acquire these skills, is expected. Effective oral and written
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 5 hours ago
professionals and biomedical researchers from all backgrounds by facilitating learning within innovative and integrated curricula and team-oriented interprofessional education to ensure a highly skilled workforce
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-augmented generation (RAG) approaches Systems and mathematical modeling of biological or complex systems Natural language processing and machine learning Data harmonization and integration Record of research
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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intelligence, data science, medical physics, neuroimaging, bioengineering, or related disciplines, accompanied by accredited training in machine learning, deep learning, or medical image analysis. Experience: A
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. Position Overview The successful candidate will develop and apply advanced computational and machine learning methods to large-scale genomic, clinical, and imaging datasets, working across one or more of the
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and uncover yet unknown physiological particularities of sleep and other human health factors. Your Profile PhD (or near completion) in Biomedical Engineering, Computer Engineering, Computer
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and expectations Requirements : We are looking for a researcher who is initiative and self-driven, enthusiastic in solving biomedical problems, and rigorous in their scientific work. A doctoral degree
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echocardiography dataset called CAIFE consisting of both healthy and abnormal fetal heart scans. You will be responsible for the design and testing of original machine-learning based methods for fetal heart