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models, or related machine learning approaches. Background in cancer biology, cancer genomics, or clinical data analysis in oncology Preferably a strong publication record or impactful first author paper
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survival data using longitudinal features, and (6) machine learning and deep learning for analyzing time-to-event outcomes, or (7) radiomics and medical imaging analysis. Required Qualifications: We seek
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position to develop and apply advanced analysis methods, including artificial intelligence and machine learning algorithms and approaches, for x-ray science and instruments. These methods will accelerate
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determining fetal growth and long-term health. The research group employs physiological, molecular and translational approaches, and utilizes a wide variety of model systems including primary human trophoblast
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&M AgriLife is uniquely positioned to improve lives, environments and the Texas economy through education, research, extension and service. Click here to learn more about how you can be a part of
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, and machine learning. Your responsibilities will include publishing research in leading conferences and journals, contributing to grant proposals, and providing technical leadership in these cutting
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
, and public service. Position Summary Develop highly curated datasets of chemical bioactivity and build statistically validated and externally predictive machine learning models of these activities
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Computational Biology, Machine Learning and Deep Learning for Computational Biology, Computer Science, Applied Physics and Mathematics, or a related field. - Strong background in protein modeling, structural
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, Economics, or a related field, earned within the past six years Strong computational and statistical skills Experience with large-scale data analysis and machine learning Proficiency in scientific programming
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis