Featured RNA-Seq Job – Assistant Professor – RNA Computational Biology

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 tenure track. Expertise is required in the specific area of computational biology with a focus on RNA bioinformatics.

  • Teaching responsibilities may include participating in medical school basic science education as well as graduate training through affiliation with one or more of the inter-departmental, interdisciplinary graduate groups of the Biomedical Graduate Studies (BGS) program.
  • Clinical responsibilities may include providing essential contributions to the clinical programs of the department, including substantial teaching and/or independent contributions to clinical research programs. Publications may derive from clinical observations or from participation in studies. Both non-clinicians and clinicians are invited to apply.
  • Research or scholarship responsibilities may include developing an independent research program, synergistic with the scientific interests of the division of Neuropathology and of the Department of Pathology and Laboratory Medicine, especially in the area of RNA biology.
  • Applicants must have an M.D and/or Ph.D or equivalent degree. PhD applicants must have a doctorate in computer science, bioinformatics, biostatistics or a closely related discipline with demonstrated excellence in research and education and a strong record of collaboration.
  • The successful applicant must have a clear record of productivity and creativity during their post-doctoral training, and high likelihood of developing an independent, cutting edge program in RNA computational biology.
  • Expertise and interest in the ongoing basic and translational investigations of RNA processing and function, RNA binding proteins, small RNAs and Ribonucleoproteins will be highly beneficial.

We seek candidates with strong informatics background and deep expertise in developing advanced statistical, computational and machine learning tools for large-scale data analysis, modeling and/or simulation of RNA biology and/or gene expression regulation and dysregulation.

Examples include the development of novel computational tools to infer and extract biological and mechanistic, molecular insights from multimodal, high throughput sequencing data (e.g. CLIP-Seq, RNA-Seq, Ribo-Seq etc).

We seek candidates who embrace and reflect diversity in the broadest sense.


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