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Field
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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models
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bioinformatics, and statistical modeling to decode the complex molecular mechanisms that shape human vision. By leveraging high-dimensional data and cutting-edge computational analyses, we aim to uncover
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methods that aim to transform scientific discovery and leverage high-performance computing. Specifically, this will include research in : 1. Developing large-scale agent-based and other complex systems
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-Performance Computing (HPC) systems are essential infrastructure for solving complex scientific and engineering challenges. However, the rapid growth of HPC alongside AI is creating an urgent sustainability
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Biology, or relevant program. · Prior successful working experience with tissue cultures and various molecular biology techniques. · All applicants for this position must be authorized
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innovative strategies to improve human health. HT is composed of five Centers: Health Data Science, Genomics, Computational Biology, Neurogenomics and Structural Biology. The Centers work together to enable
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innovative strategies to improve human health. HT is composed of five Centers: Health Data Science, Genomics, Computational Biology, Neurogenomics and Structural Biology. The Centers work together to enable
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 13 hours ago
fellow to participate in his inflammatory bowel diseases (IBD)-focused computational genomics research program, part of a collaboration with Dr. Shehzad Shiekh, MD, PhD, faculty in the Division
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About the Role You will develop and apply novel computational methods to quantify the societal impact of fundamental science discoveries. Candidates close to completion of their PhD will initially
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or projects under control or supervisory responsibility. 2. Recommends changes in research, testing or experimental procedures. 3. May be responsible for a single highly technical and complex piece of research