62 phd-in-mathematical-modelling-of-biochemical-reactions Postdoctoral positions at Duke University
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, United States of America [map ] Subject Areas: Computer Programming Systems Modeling Biochemistry Atmospheric Science Appl Deadline: (posted 2025/02/19, listed until 2025/05/01) Position Description: Apply Position
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by two NIDA-funded grants. The first project, NEURONIC, utilizes a nasal spray paradigm to assess reactions to nicotine in a controlled, laboratory setting as a model of risk for addiction
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. Responsibilities/Duties: · Perform biostatistics and bioinformatics for scRNA seq analysis. · Perform molecular, cellular, biochemical and immunological analyses · Optimize and troubleshoot experimental protocols
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outside Duke University. Preferred qualifications: PhD (completed in the last 1-5 years or PhD candidate) in a quantitative discipline, including Computational Biology, Bioinformatics, Computer Science
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mathematics and engineering. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state
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pathways and mechanisms underlying autoimmunity from a lncRNA and epigenetic gene regulation perspective. We utilize biochemical assays, tissue culture, mouse transplantation & disease modeling experiments
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Assessment Models (IAMs) such as GCAM or PAGE. The candidate must have a PhD degree in a related field, be fluent in computer programming, preferably python, and will ideally have experience in working with
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differentiation and melanoma and multiple myeloma biology utilizing cultured cells and animal models of skin diseases. Work Performed • Development of new and implementation and modification of existing
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on-the-ground field surveys, water quality monitoring, drone surveys, digital twins and ecosystem service modelling to generate estimates of ecosystem functions and services provided by a living shoreline that is
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collaborative environment at Duke is ideal for our multi-scale modeling research efforts. An earned PhD and previous experience in computational neurostimulation modeling are required as are excellent