79 phd-in-mathematical-modelling-population Postdoctoral positions at Duke University
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uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments. • Collaborate with mathematical modelers and experimentalists in
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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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spans basic science models, including primary tissues and mouse models, as well as translational clinical research. Qualified candidates will have a strong record of training in biomedical research
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rising, highly ranked engineering schools in the nation. The school consists of four departments with 130 tenure-track faculty members, 1250 undergraduate students, 1400 master’s students, and 600 PhD
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related field • Strong quantitative background (e.g. ecological theory and mathematical modeling, hierarchical statistical modeling, machine learning, remote sensing, geospatial statistics) • Demonstrated
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Nursing (MSN), Doctor of Nursing Practice (DNP), and PhD in Nursing. Our programs are designed to meet the evolving demands of today’s healthcare landscape, equipping graduates with rigorous training
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science. Other application areas of interest include robust parameter estimation and performance bounds under model misspecification, integrated sensing and communications, orthogonal time frequency space
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requires working with large data sets related to parenting and child development across multiple sites in the United States, helping to prepare data and estimate models for a variety of research papers, and
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will develop and apply population-level, natural history models of uterine cancer. The fellow will join established, collaborative teams at Duke University. This position is ideal for a highly skilled
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biology, and mechanisms of drug resistance. Research activities will utilize a combination of in vitro approaches, including cultured cell models, and in vivo experimental systems using animal models