Computational Chemistry/Informatics Postdoctoral Fellow, Early Translation Branch, Division of Preclinical Innovation

Description

NCATS, a major research component of NIH, seeks applications from outstanding candidates to fill a computational chemistry/informatics postdoctoral fellow position in the Early Translation Branch (ETB) of its Division of Preclinical Innovation. The selected postdoctoral fellow will work under the mentorship of a senior computational group leader in a team environment with diverse focuses within the ETB. They will focus on applying state-of-the-art computational chemistry techniques—including molecular modeling, molecular dynamics, artificial intelligence/machine learning (AI/ML) and virtual screening—to help design and develop new therapeutic agents. The successful candidate will have the opportunity to contribute to high-impact projects and work closely with teams with varied disciplines.

Core Responsibilities

The selected candidate will:

• Conduct structure-based and ligand-based drug design studies to identify novel bioactive compounds.

• Apply molecular docking, molecular dynamics and free-energy calculations to assess compound binding and stability.

• Develop and implement AI/ML models to predict the bioactivity/pharmacological properties of compounds and optimize lead compounds.

• Use virtual screening techniques to evaluate large chemical libraries for potential drug candidates.

• Work closely with experimental scientists to guide compound synthesis and biological testing based on computational findings.

• Analyze high-throughput screening data and incorporate cheminformatics approaches to enhance hit identification and lead optimization.

• Present research findings in internal meetings and at external scientific conferences and contribute to peer-reviewed publications.

Qualifications

Applicants should possess:

• A doctorate in computational chemistry, cheminformatics, computer sciences, or a related field

• Strong expertise in molecular modeling techniques, such as molecular docking, molecular dynamics simulations and free-energy perturbation methods

• Familiarity with AI/ML algorithms and experience using these methods in computational chemistry or drug discovery

• Proficiency in programming and scripting languages (e.g., Python, R) and experience with relevant software (e.g., Schrodinger, OpenEye, MOE)

• Strong analytical skills, with a solid grasp of cheminformatics and data-driven approaches to drug discovery

• The ability to work both alone and in a team environment with diverse focuses

• Excellent verbal and written communication skills, with a record of publications in peer-reviewed journals

Salary/Benefits

Annual stipends are commensurate with experience and based on the NIH Postdoctoral Intramural Research Training Award and Visiting Fellow scale; medical insurance coverage will be provided. The fellow also may participate in Foundation for Advanced Education in the Science courses at NIH. The position is renewable for up to five years.

How to Apply

Please submit a cover letter that includes a research summary and describes your interest in the position, a current curriculum vitae with a complete bibliography, and contact information for at least three references to Min Shen, Ph.D.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.

Application Link:https://ncats.nih.gov/about/jobs/computational-chemistryinformatics-postdoctoral-fellow-early-translation-branch-division?utm_source=X&utm_medium=Social&utm_campaign=jobs