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(SUDs), psychiatric conditions, and other behavioral and lifestyle characteristics that impact human health using large datasets and biobanks including the Million Veteran Program (MVP), the SUD working
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inflammation, multiparameter flow cytometry, and bioinformatics/computational biology is desired. Please send curriculum vitae, three names of reference and a one-page summary of research background and
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of Genomic Health and Department of Genetics. Our lab uses computational and experimental methods to understand how human genetic variation underlies health and disease, across rare and common phenotypes, with
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where it would be cost-effective to screen and (iii) incorporating multi-omics data to better identify at-risk individuals beyond lifestyle and environmental approaches alone. Our research program has
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, computer science, data science, social sciences, public policy, or a relevant interdisciplinary area. Interest in addressing the governance, ethical, legal, and societal implications (GELSI) posed by digital
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. Qualifications We are seeking candidates who meet the following criteria: A PhD or equivalent degree, already obtained, in a related field e.g., philosophy, law, computer science, data science, social sciences
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high impact careers in biomedical research. This is an opportunity for a highly motivated individual with a background in population genetics, computational biology, or a related area. Candidates with
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program of training will also include both attendance at departmental workshops and seminars. They will present their results at weekly Fredericks Lab meetings, join in our weekly journal club, and be
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of mutational processes in human health and disease. We are an interdisciplinary team of experimental, computational, and clinician scientists allowing us to generate and analyse complex data from emerging
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modeling. However, interested candidates with a strong computational background and interest in getting involved in medical imaging and preclinical models are also strongly encouraged to apply. A PhD in