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accordance with MCW/FMLH/CSG policies and procedures. The APP fellow will have a strong foundation in evidence-based practice and ensure an environment of safe, quality care. Learn More: General APP Fellowship
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approaches, machine learning) where appropriate. The successful candidate will actively promote FAIR data practices and will have opportunities to contribute to teaching, training, and wider community
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tools such as R, Python, or MATLAB as well as relevant machine learning frameworks Experience in statistical data analysis, and expertise in areas such as experimental design, linear/nonlinear models
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on resistance mechanisms in breast cancer, glioblastomas, colon cancer, and lung cancer. Advancing precision oncology through machine-learning models: We integrate multimodal patient data, including multiomic
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of remote sensing data using physical, statistical and/or machine learning approaches Knowledge of the latest remote sensing techniques, key satellite missions (e.g. Copernicus) and their application
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the High Performance Computing and Data Center, to be completed on campus by August 2026. This position collaborates with others in the growing machine learning and exoplanet subgroups within the Physics and
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The Level A Research Fellow will contribute to the research efforts of the Department of Electrical and Computer Systems Engineering, with a specific focus on advancing a cutting-edge research program in
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a track record in computational modelling that explores the dynamics of AI systems and the development of autonomous AI agents, experience with machine learning, reinforcement learning, and generative
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | 2 months ago
both the NCRC in Kannapolis and UNC Charlotte in Charlotte. Experience in yeast genomics, RNA-sequencing, bioinformatics, or machine learning is preferred. Departmental Preferred Experience, Skills
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challenges from low carbon shipping and sustainable fuels to solar power technologies and advanced brain models. Learn more at https://mecheng.ucl.ac.uk . Within this dynamic environment, the Moazen Lab is