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application to lineage tracing Algorithms for characterizing structural alterations in bulk and single cell whole-genome data Mutational signature analysis for cancer/brain samples Analysis of repetitive
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
is important to note that this position is contingent on T32 funding. Education and Experience: PhD in Computer Science, Bioinformatics, Computational Biology, or related discipline. Knowledge of
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to computational modeling, simulations, and advanced data analytics for scientific discovery in materials science, biology, astronomy, environmental science, energy, particle physics, genomics, and other scientific
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, machine learning or AI to computational modeling, simulations, and advanced data analytics for scientific discovery in materials science, biology, astronomy, environmental science, energy, particle physics
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of classification algorithms Correlate/Integrate In Vivo and Ex Vivo metabolite analysis to understand the key metabolic pathways in different tumour types and subtypes Identify and harmonise MRI and MRS acquisition
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web technologies Experience in teaching bioinformatics Previous experience with AI and/or machine learning approaches Interest in reproductive health and/or development of clinical tools and algorithms
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treatment and reducing brain injuries Modern MRI scans tell us about a tumour’s biology. Through advanced computing (radiomics), it is possible to extract much more information from MRI images than is visible
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the COVID-19 vaccines) is a highly dynamic and fast moving field. We have developed a generative AI algorithm to optimize the mRNA, and we are looking for an experienced wetlab researcher to test the
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research and teaching. The Division has a diverse portfolio addressing all areas of biology from protein interactions to cell function, organism development, genetics, population studies and the environment
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apply cutting-edge machine learning algorithms, with focus on foundation models and LLMs/agents, to analyze complex biological data. This data includes gsingle cell genomics profiles, spatial data, and