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algorithms for image analysis to facilitate and optimize early disease screening, diagnosis, and treatment. Ideal candidates should possess a M.S. or PhD (or equivalent) in computer science, mathematics
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viruses, bacteria, parasites, and fungi, with emphasis on host-pathogen interactions, immunology of infections, cellular microbiology, and evolutionary dynamics within the microbiome. The MMG faculty
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, to understand molecular mechanisms of corresponding disorders. We have also developed network analyses frameworks to characterize the evolutionary dynamics of these protein networks. Another dimension of our past
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of early pre-malignant development of ovarian carcinoma and development of early detection/screening tests/algorithms of ovarian cancer. Qualified candidates must have a Ph.D. degree in Biology, Cellular
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revealing mechanisms and therapeutic targets of early pre-malignant development of ovarian carcinoma, novel big data approaches, and development of early detection/screening tests/algorithms of ovarian cancer
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learning algorithms focused on image data, natural language processing, and tabular data Conduct exploratory data analysis and feature engineering for high-dimensional datasets, including image, text, and
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Python Proficiency: Demonstrate expertise in Python or a similar high-level programming language is essential for developing algorithms and backend logic Azure DevOps Experience: Familiarity with Azure
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materials, implementing algorithms, creating software, and investigating new approaches and technologies. Assists with conducting research, including setting up, calibrating, maintaining research records
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to create analytic data cohorts (study cohorts). Apply machine learning methods on clinical datasets to identify predictive factors – selecting algorithms, preprocessing data, training models, and evaluating
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been fully upgraded. Preference will be given to applicants who work on the basic biology and evolutionary trajectories of emerging pathogens, use and develop novel animal models of human disease, design