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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 18 hours ago
healthcare professionals and biomedical researchers from all backgrounds by facilitating learning within innovative and integrated curricula and team-oriented interprofessional education to ensure a highly
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) better than rate-based scores. We further hypothesize that these properties covary with genotype. This project aims to (1) assess and understand the potential structure of withdrawal, (2) acquire data from
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across a range of application areas, including education and healthcare. As these systems are increasingly deployed in high-stakes environments, there is a growing need for machine learning models
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Research Initiative on the History of Sexualities and is expected to participate in their activities and to teach one course during the fellowship. Compensation is commensurate with education and experience
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Potentials (MLIP), machine learning (ML) predictive models and AI tools. Activities : Computer science implying ML and AI tools applied to material science Where to apply Website https://umontpellier.nous
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profiling, molecular biology, cancer genomics, and in vivo preclinical modeling. Examples of representative recent papers led by wet-lab scientists in our lab (as first author) include: https://www.nature.com
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chromatography. The candidate will prepare samples for immunopeptidomics by liquid chromatography coupled with mass spectrometry tandem (LC-MS/MS), analyze large datasets, and develop scripts and machine learning
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Appointment Dates: July 1, 2026, to June 30, 2027 Proposed Compensation: $68,500 or commensurate with experience. Email/Website: [email protected] , https://www.reilly-lab.com(Link is external) , https
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Dr. Playdon’s research, visit her faculty profile https://profiles.faculty.utah.edu/u6017378 . Details about the NIH-funded project (1R01CA313949) can be found on NIH RePORTER: https://reporter.nih.gov
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The SDU Adaptive Intelligence Lab (ADIN Lab) (https://adinlab.github.io/ ) located under the Data Science and Statistics Section of the Department of Mathematics and Computer Science (IMADA