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. The research program embraces team science drawing from i.e., implementation science, data science, geospatial, epidemiological, and machine learning approaches to better understand broad contexts including
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refining and deploying machine learning tools. Experience interpreting biologic and informatic literature to apply principles and methods to association data for treatment response and clinical utility will
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, evaluate and make publicly available computer code following principles of reproducible research · Develop novel statistical methodologies · Maintain current knowledge of and contribute
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for both internal COH investigators and external biotech collaborators. Autologous and allogeneic projects consist of immunotherapies (e.g. CAR-T, CAR-NK, TILs) and stem cells (e.g. iPSCs, MSCs, NSCs), as
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applying interpretable AI / machine learning / deep learning / information-theoretic methods and algorithms in the context of multiscale biological networks, ranging from molecules (protein chemistry) to
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candidate, you will: Develop and apply Bayesian Network machine learning methods to analyze the dynamics of G-protein coupled receptors to uncover allosteric regulation that enables design of allosteric
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reporting facilities. Trains and assists in orienting new statistical staff. Monitors project progress and maintains division records and files. Documents computer programs and electronic databases. Engages