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Field
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quality models to better understand the effect of soil health management practices on soil-water storage in agricultural fields. A postdoctoral position is available to examine the effect of soil health
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in AI-modeling, Monte Carlo simulation, and coding. The successful candidate will work alongside a multidisciplinary team, leveraging artificial intelligence and computational methodologies to optimize
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 10 hours ago
Posting Information Posting Details Department Renaissance Computing Inst - 637100 Posting Open Date 08/05/2025 Application Deadline Open Until Filled Yes Position Type Postdoctoral Scholar Position
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About the Role We are seeking an experienced and highly motivated senior postdoctoral researcher in computational biology. The successful candidate will join a multi-disciplinary team working in
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with statistical modeling (ideally Bayesian statistics) • Proficiency in Fortran, R, Python, Matlab, or ideally other common languages (e.g., C/C++) Strong computational skills Strong oral and written
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the implications of connecting wearable computing devices such as smart glasses to artificial intelligence large language models. The exact contribution of the post-holder will depend on their background and
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, predictive models of neurodegenerative disease with a focus on Alzheimer's Disease. Computational models will be developed that utilize data obtained from a wide range of experiments, from basic biochemical
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Registry (SCNIR), enabling clinically driven translational research. We employ state-of-the-art experimental models to study the pathomechanism of severe congenital neutropenia and Shwachman-Diamond Syndrome
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About the Role We are seeking an experienced and highly motivated senior postdoctoral researcher in computational biology. The successful candidate will join a multi-disciplinary team working in
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techniques may be employed to support the modeling of uncertainty, along with the formulation and resolution of planning problems using stochastic optimization methods. Key Responsibilities The selected