Postdoctoral Informatics Scientist, Division of Preclinical Innovation, NCATS Chemical Genomics Center (NCGC), Adenine Informatics Group
Description
NCATS is one of the 27 institutes and centers at NIH. The Informatics group collaborates closely with biologists and chemists to develop robust assay designs, analyze experimental outcomes and validate new hypothesis. NCATS’ informatics personnel perform a variety of ligand- and protein structure-related modeling tasks, support the drug discovery process and accelerate translational sciences. The team members also are developing infrastructure and software for the meta-analysis of high-content screens and for integration of the existing biomedical data to discover new hypothesis.
NCATS’ Informatics group seeks a creative, self-motivated postdoctoral informatics scientist to develop new algorithms for prediction of i) protein-ligand interactions; ii) chemical reactions and comprehensive support of drug discovery projects.
Core Responsibilities
The postdoctoral fellow will be responsible for uploading and analyzing pilot and quantitative high-throughput screening runs; analyzing assay reproducibility; analyzing confirmation and secondary screening assays; developing and applying QSAR models and molecular modeling for virtual screening of compounds; supporting medicinal chemistry efforts; and developing new algorithms for prediction of protein-ligand interactions and chemical reactions. Research will be conducted by NCATS' Informatics group.
Qualifications
The ideal candidate will possess a minimum of a Ph.D. in computational science/bioinformatics with specialization in machine learning. He or she will have published at least three research articles as first author. The successful candidate should have experience with common cheminformatics libraries and data formats (e.g., RDKit, OpenBabel, SMILES, InChi, etc.), modern software packages (e.g., MOE/CCG, OpenEye tools) and scientific programming (e.g., Python, JAVA) and data analytics (e.g., KNIME, Spotfire). He or she should be able to script work flows and implement new algorithms, have experience working with multi-disciplinary teams, and possess strong oral and written English communication skills. He or she should have experience using modern machine learning and deep learning frameworks (e.g., TensorFlow, Keras), as well as experience in developing algorithms for prediction of protein-ligand interactions and/or chemical reactions.
Applicants should be U.S. citizens or legal residents or should hold a valid work permit to be considered for the position.
How to Apply
Please submit a cover letter describing your interest in this position, a current curriculum vitae with a complete bibliography to Alexey V. Zakharov, Ph.D., at alexey.zakharov@nih.gov.
Application reviews will begin promptly and continue until the position is filled.
Additional Information
A preappointment process (e.g., background investigation, verification of qualifications and job requirements, completion of onboarding forms, submission of required documents) may determine employment after an offer has been made and accepted.
At your supervisor’s discretion, you may be eligible for workplace flexibilities, which may include remote work or telework options and/or flexible work scheduling. These flexibilities may be requested in accordance with the NIH Workplace Flexibilities policy.