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underlying juvenile dermatomyositis and related pediatric conditions. Through the integration of advanced molecular techniques, patient-derived samples, and functional muscle modeling, you will help develop
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on identifying and validating surrogate endpoints for overall survival using data from cancer clinical trials and patient registries, developing prognostic models of clinical outcomes in cancer, and conducting
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computational immunology as part of the NIH/NIAID-funded Multiscale Immune System Modeling (MISM) Center. The postdoctoral associate will contribute to and participate in meetings, workshops, trainings
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computational models and reproducible analytical pipelines. Validate automated behavioral measures against expert human coding standards. Develop approaches that support objective measurement of caregiver
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to expand their scientific expertise while contributing to meaningful discoveries in vision science. What You’ll Bring Required Qualifications PhD or MD/PhD in a relevant scientific discipline. Strong
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. or equivalent doctorate (e.g. Sc.D., M.D., D.V.M.) Preferred Qualifications:. A PhD or MD/PhD (or equivalent) in biological sciences Strong research background in cell biology, molecular biology, mouse models
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development of measurement, evaluation, and analytic strategies to inform policy and systems transformation – 45% Develop conceptual frameworks, logic models, measurement plans, and analytic strategies, drawing
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meetings, contribute to grant applications, and build an independent research career in cancer immunology and neuro-oncology. Minimum Requirements PhD, MD, MD/PhD, or equivalent doctoral degree in immunology
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therapy, with a particular emphasis on epigenetic regulation, radiation response, and treatment resistance. Our work leverages patient-derived organoids, engineered cellular models, functional genomics, and
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. This position is designed for scientists with strong computational and quantitative training who are interested in agent-based modeling, network science, infectious disease dynamics, uncertainty quantification