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computational models of the immune response for multi-scale epidemic models. This position offers an excellent opportunity for recent graduates interested in applying quantitative and computational methods
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inhalation exposures. We use in vitro, rodent, and non-rodent models to achieve our study objectives. We will employ various techniques/methods such as ELISA, multiplex MSD panel assays, flow cytometry, bulk
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The Department of Biostatistics & Bioinformatics at Duke University invite applications for a postdoctoral associate position in mathematical modeling, as part of a new NIH-funded Center for Excellence in
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on cell viability and DDR activation in established human cell models. The student will perform CRISPR screens to determine factors that affect resistance/sensitivity and follow these up with mechanistic
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processes associated with CIN [1], leveraging single-cell DNA sequencing understand CIN heterogeneity [2], and development and implementation of machine learning and AI models to imaging data [3]. The student
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: Advanced molecular and protein analysis Mass spectrometry-based imaging Multi-omics technologies Preclinical cardiometabolic animal models They will also gain professional development in data stewardship
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treatment processes through advanced machine learning, validated against physics-based models and experimental data. System Integration: Integrating the DTs into material and energy balance equations
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model development to analyse the cost-effectiveness, cost-benefit and/or budget impacts of treatment strategies, and conduct sensitivity analyses and scenario planning Source appropriate epidemiological
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. As part of this PhD, the candidate will: Conduct an integrative review of established competency models Create assessment tools (which may include use of AI tools) to measure CLMA proficiency
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models based on survey and secondary data. Creative approaches to combining and leveraging various data sources are highly encouraged! The selected Ph.D. candidate will benefit from our outstanding