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biologically-constrained machine learning–based model discovery pipelines to derive interpretable surrogate ODE/PDE models from simulated ABM data and spatial-omics data collected from state-of-the-art
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candidate will contribute to projects involving participants with implanted intracranial electrodes and wearable non-invasive sensors, with a focus on memory consolidation, spatial navigation, and neural
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managing human subjects research and handling sensitive data is preferred. • Strong quantitative skills, including proficiency in regression modeling, environmental mixtures analysis, and spatial methods
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including but not limited to microbial ecology, biochemistry, genomics, biostatistics, molecular biology, microbiology, evolutionary biology. Familiarity with metagenomics data analysis, microbial
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of Civil and Environmental Engineering / Chaney Lab: Perform the core of the proposed research activities including processing the remotely sensed LST to compute the spatial statistics, run the HydroBlocks
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will develop novel statistical and machine learning methods for any of the following: multi-omics data (such as bulk and large-scale single-cell RNA sequencing data, spatial transcriptomics, bulk and
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manipulation, viral gene therapy, pharmacological studies, gene editing, and physiological measurements of cardiac electrical and mechanical function at a variety of spatial scales from a single cell to whole
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& Behavioral Sciences at Duke University School of Medicine. Our research combines laboratory behavioral pharmacology, ecological momentary assessment, and functional neuroimaging to examine neurobehavioral
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or natural resource economics, ecological economics, or a related field. • Background in environmental modeling, programming, and LCA tools • Demonstrated ability to work collaboratively on cross-disciplinary
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interaction and physical activity effects on heart rate and energy expenditure in wild baboons in Amboseli National Park, Kenya. Human Ecology, Energetics, and Climate: This postdoc will work as part of a team