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maternal metabolism in pregnancy. The position involves analysis of large-scale genomic data, including genome-wide association studies (GWAS), computation of genetic risk scores, and integrative modeling
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qualification in Genetics, Bioinformatics, Computer science, Data science, Statistical Genomics or a related discipline involving the interrogation of ‘omics’ datasets. Hands-on experience with large-scale human
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be involved in projects to perform big-data research, bundling genetics, multi-omics, biomarker, clinical, and histopathological data, to harness the full range of biologically relevant data to better
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data and high-throughput, multi-dimensional -omic data to study neurodegeneration and diseases of the central nervous system, with an emphasis on Alzheimer disease, Parkinson’s disease, and other
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include experience with fiber sensing, machine learning tools, and big data workflows. Instructions To apply, candidates will submit materials via Interfolio, comprising (1) a letter of interest describing
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of the tremendous variability in landscape, fire, and treatment characteristics to conduct a set of natural experiments, taking an approach based on “big data” and remote sensing to learning from past fuel treatment
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both dry and wet lab operations. Dry Lab Operations: Analyze large-scale datasets including single-cell and bulk RNA sequencing, microbiome data, proteomics, and other high-throughput platforms. Apply
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participants using cognitive and/or neuropsychological tests or survey instruments. Manages all aspects of large dataset: data synthesis, tracking of research participants extracting datasets for other
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tissue, new imaging modalities, small animal maintenance and surgeries, and single cell transcriptomics. They will also gain experience in bioinformatic analysis of sequencing and large-scale imaging
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Primary Duties & Responsibilities: Information on being a postdoc at WashU in St. Louis can be found at https://postdoc.wustl.edu/prospective-postdocs-2/ . Trains under the supervision of a faculty mentor