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to define and lead specific research objectives aligned with the funded aims. Responsibilities will include project management, coordination of data collection and analysis, manuscript preparation, and
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approaches to understand the interconnections between nutrition, health, food systems, and society. Our lab has pioneered genomics techniques that objectively track dietary intake across hundreds of plant and
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for diverse social and homeostatic states. The lab uses a combination of circuit-based strategies and custom engineered designs to investigate how social and non-social salient information is encoded in neural
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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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/bioinformatics, and data science. Work Performed · Work in highly collaborative inter-disciplinary environment with clinicians, econometricians, statisticians, and data scientists · Lead statistical analysis
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of physical activity on energy expenditure, energy balance, and health outcomes. These two positions are: Non-Human Primate Activity & Physiology: This postdoc will work as part of a team investigating social
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Bioinformatics expertise required for scRNAseq analysis. · Previous cell culture experience. · Perform molecular, cellular, biochemical and immunological analyses. · Optimize and troubleshoot experimental
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: • Experience with geochemical speciation methods, particularly synchrotron X-ray spectroscopy • Knowledge of data management • Experience with statistical analysis of data and geospatial analysis tools (e.g
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. Engage in a spectrum of research activities including literature review, experimental design and execution, data analysis, manuscript preparation, communications with collaborators and journal editors, and
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statistical models to investigate gene by environment interactions and to utilize bioinformatics resources and high-dimensional –omics data to elucidate the biological significance of the statistical analysis