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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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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 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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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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? Collaborate with nationally recognized leaders in radiology, machine learning, and imaging science. Access world-class research resources and interdisciplinary partnerships. Expand your professional network
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for genomics (e.g., generative models, transformers, agentic workflows) and/or statistical learning (e.g., network & spatiotemporal modeling, functional/longitudinal data, time-series). Analyze single-cell
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. Candidates with background knowledge and hands-on experience in mouse models, proteomics, 3Dorganoids,primary cells purification and culture skills are particularly welcome. Minimum Requirements: Ph.D
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characterizing the endocrine-disrupting potential of plastic additives, with a primary emphasis on androgen receptor (AR) and estrogen receptor alpha (ERα) modulation, using established in vitro and in vivo models
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background in molecular biology and cell biology. Experience working with animal models. Demonstrated scientific curiosity, initiative, and commitment to research excellence. Ability to work independently
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cancer models and patient-centered translational approaches to define mechanisms that regulate treatment response, immune evasion, and disease progression. Candidates with experience in cancer biology