Postdoctoral Research Scientist in Systems Pharmacology

National Institute of Health (NIH/NLM/NCBI)
Job Location
Bethesda, MD
Job Description

Dr. Philip Bourne’s research group at the National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH) which is a part of the Department of Health and Human Services (DHHS) is seeking a Postdoctoral Research Scientist in the fields of Systems Pharmacology. The candidate is expected to develop new computational methods for the multi-scale modeling of drug actions and genotype-phenotype associations. Specific roles include molecular dynamics simulation of drug binding/unbinding events, predicting genome-wide protein-ligand interactions by integrating multiple omics data, applying state-of-the-art genomics and systems biology tools to analyze gene regulation, signaling transduction, and metabolic networks, identifying individualized drug response biomarkers, and knowledge representation and ontology development of drug responses. NCBI provides an excellent environment for innovative researches in the interface between computational sciences and biomedicines.


Minimum requirements: Candidates may be U.S. or non-U.S citizens and must have a Ph.D. in biophysics, statistics, and/or computer science. The candidate also must have a strong background and experience in one or more of the following areas: molecular dynamics simulation, machine learning, or multi-variable statistics; strong programming skills using Java, C++, Python, or Perl, and familiarity with computing environments such as MATLAB and R; effective communication skills; self-motivated and independent. Successful candidates will serve on a non-competitive appointment in the excepted service. Salary and benefits are competitive, commensurate with education and experience.

Preferred qualifications: Expertise and strong publication record in one or more areas related to ENCODE, GWAS, transcriptome data analysis, protein-protein interaction network analysis, high-dimensional data analysis, big data analytics, deep/multitask/PU learning, protein-ligand docking, and metadynamics.

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