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Field
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vectors studies, including vector design and cloning. RNA sequencing. Proteomics/transcriptomics and bioinformatics. PLA. Immunohistochemistry on brain sections. Surgery to implant electrodes and EEG
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bioinformatics, molecular ecology, computational approaches to study genetic diversity and evolution, or related topics that the employer considers relevant to the service. Willingness to train and mentor students
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, qRT-PCR, marker design and validation, sequencing library preparations. Knowledge of Bioinformatics: genome resequencing and transcriptome data analyses, variant calling, QTL and GWAS analyses. A
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epidemiologic methods for environmental ‘omics and related software packages, including R, SAS, Python or equivalent. Previous experience analyzing large DNA methylation datasets, bioinformatics, and use
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are required. Skills in molecular biology, tumor sample processing, functional assays, and genomics are highly relevant (experience in bioinformatics is valued as well). WashU Medicine is a top 10 medical school
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., enhancers) throughout the genome and impact of noncoding mutations on gene expression. Your Qualifications Successful candidates should: hold a recent Ph.D. in computational biology, bioinformatics
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activity •Experience in Bioinformatics •Experience in monitoring plant and microbes for stress, nutrition, health •Experience in identifying and control plant pests and diseases Required Knowledge, Skills
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Neuroanatomy & brainstem physiology Experience with Autonomic nerve recordings or plethysmography Experience with Bioinformatics or advanced signal analysis Skills Interpersonal skills Written and Verbal
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bioinformatics a plus). Familiarity in immunology-related assays, such as flow cytometry, ELISA, or cytokine profiling. Ability to maintain accurate records and work independently on multiple projects. Familiarity
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Science, Computational Biology, Bioinformatics, or a closely related field. Experience with computational modeling, statistical programming (Python, R, MATLAB), or machine learning. Experience working with large datasets