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The Department of Pathology and Laboratory Medicine at the Perelman School of Medicine at the University of Pennsylvania seeks candidates for an Assistant Professor position in the non-tenure academic clinician track. Expertise is required in the specific area of of general molecular genetic pathology clinical practice with broad experience in the review and interpretation of massively parallel sequencing data, advanced genomic analytes, and molecular and genomic test development. A strong interest in bioinformatics and biomedical informatics with experience creating analysis pipelines for NGS and an emphasis on improving the accuracy, efficiency, and automation of data processing, data visualization, variant curation, and clinical reporting is highly desirable. Proficiency in multiple programming languages, especially R and Python used in software development, will be favorably regarded. D evelopment and/or direct experience with advanced genomic applications, such as ctDNA or genome-wide copy number analysis, is also highly desirable.

Teaching responsibilities may include didactic lectures as well as case-based sign-out teaching with residents, fellows, and medical students in general areas of genomics as well as bioinformatics.

Clinical responsibilities may include review, interpretation, and sign-out of DNA and RNA molecular assays including massively parallel sequencing and targeted molecular pathology assays. Additional responsibilities may include design, validation, and help with implementation of new molecular and genomics assays.

We seek candidates who embrace and reflect diversity in the broadest sense. The University of Pennsylvania is an EOE. Minorities/women/individuals with disabilities/protected veterans are encouraged to apply.

The University of Pennsylvania is an equal opportunity employer. Candidates are considered for employment without regard to race, color, sex, sexual orientation, religion, creed, national origin (including shared ancestry or ethnic characteristics), citizenship status, age, disability, veteran status or any class protected under applicable federal, state, or local law.