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CPTAR - Open Rank (Assistant/Associate/Professor)
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Contribute to the School’s mission through research, clinical practice (if applicable), and teaching in the DDS/PhD Program and/or graduate level courses as…

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Translational Science Interagency Postdoctoral Fellowship (Cheminformatics/AI-ML)

National Institutes of Health -
Bethesda, MD

Apply Now

Job Details

Full-time

Qualifications

Programming languages
Computer skills
Statistical analysis tools

Full Job Description

The NIH National Center for Advancing Translational Sciences, Division of Preclinical Innovation, in collaboration with the FDA Division of Applied Regulatory Science, is now seeking a qualified postdoctoral fellow to join our Early Translation Branch.

About the position

The National Center for Advancing Translational Sciences (NCATS), a major research component of the National Institutes of Health (NIH), invites applications for a postdoctoral fellow position through the Translational Science Interagency Fellowship (TSIF) program, a three-year long collaborative training program between NCATS and the U.S. Food and Drug Administration (FDA).

This position offers a unique opportunity to work at the interface of computational modeling, drug discovery, and regulatory science, with direct exposure to how computational models and human-relevant experimental platforms can influence real-world drug development and safety evaluation. The fellow will be co-mentored by investigators at NCATS and FDA and will contribute to developing AI/ML-driven approaches for mechanism-based drug safety assessment and drug repurposing , as well as experimental validation using cutting-edge New Approach Methodologies (NAMs) and complex in vitro models (CIVMs). Fellows are expected to spend three years in combined training at the FDA and NCATS. The exact timing of the transition between the two agencies will be project and mentor dependent.

Responsibilities of this position include:

Curate, harmonize, and analyze a large-scale target class profiling datasets and construct compound-level activity signature vectors
Design, develop, and optimize multi-task machine learning models to predict target-specific and cross-target inhibition patterns
Integrate target class activity signatures within silico ADME and organ toxicity predictions into a unified compound prioritization framework
Actively participating in laboratory-based research activities utilizing human-relevant in vitro models.
Applying CIVMs and other NAMs to experimentally evaluate and confirm computational model predictions related to organ-specific adverse events, including hepatotoxicity, cardiotoxicity, and CNS toxicity
Collaborate closely with experimental scientists, regulatory science colleagues at FDA, and interdisciplinary teams across NCATS
Present research findings in internal meetings and at external scientific conferences and contribute to peer-reviewed publications.

Training & Development Opportunities:

Receive comprehensive training in human induced pluripotent stem cell-derived techniques, gaining hands-on experience with cutting-edge cellular models used in regulatory science
Gain an in-depth understanding of how human-relevant in vitro models advance the principles of regulatory science and public health protection
Develop expertise in emerging NAMs and AI/ML methodologies with direct regulatory application
Gain meaningful exposure to government regulatory science and the drug evaluation process at one of the world's leading public health agencies
Build a professional network spanning NIH, FDA, and the broader translational and regulatory science communities

What you'll need 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., at [email protected] .

Application reviews will begin promptly and continue until the position is filled. Fellows must be onboarded non later than September 21, 2026.

Contact name

Min Shen

Contact email

[email protected]

Qualifications

Prospective applicants should possess a Ph.D. in computational chemistry, cheminformatics, computer science, data science, , bioengineering, pharmacology or a related discipline, with demonstrated experience in data-driven research, including machine learning or statistical modeling. Strong programming skills in languages such as Python or R are required, along with excellent written and oral communication skills and the ability to work both independently and collaboratively in a multidisciplinary research environment. Experience working with large-scale biological or chemical datasets, QSAR modeling, or familiarity with AI/ML methods and/or in vitro experimental platforms is preferred.

This position is not eligible for full-time remote work, and NIH does not permit trainees to telework from overseas locations.

Disclaimer/Fine Print

Applicants must meet the following qualifications:

Have a Ph.D., M.D. or other doctoral degree in a related discipline or have documentation that all degree requirements will be completed before the start of the fellowship in September 2026. Assurance to this effect must be supplied in writing by the chair of the dissertation committee (for Ph.D. candidates) or the dean of the medical school (for M.D. candidates).
Be a citizen or permanent resident of the United States at the time of application. Applicants must have resided in the United States for three of the last five years.
Be within two years of their terminal degree and have no more than two years of prior postdoctoral training before joining this fellowship program.
Be able to pass a federal background check using Standard Form-85 (or SF 85). Section 14 of the form asks, “In the last year, have you used, possessed, supplied or manufactured illegal drugs?” The question pertains to the illegal use of drugs or controlled substances in accordance with federal laws, even though permissible under state laws. Applicants will be required to complete a conflict-of-interest assessment.
If an NCATS/FDA Translational Science Interagency Fellow, to include their spouse and minor children, reports what is identified as a Significantly Regulated Organization (SRO) or prohibited investment fund financial interest in any amount, or a relationship with an SRO, except for spousal employment with an SRO, and the individual will not voluntarily divest the financial interest or terminate the relationship, then the individual is not placed at the FDA. For additional requirements, see FDA Ethics for Nonemployee Scientists ( https://www.fda.gov/about-fda/office-chief-scientist/fda-ethics-non-employee-scientists)

Apply Now

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ncats nih jobs

Translational Science Interagency Postdoctoral Fellowship (Cheminformatics/AI-ML)

CPTAR - Open Rank (Assistant/Associate/Professor)

QA/QC Navigator

Lennon Distinguished Professor - Epidemiology

Center Director - Center for Regenerative Sciences - Open Rank (Associate Professor/Professor)

Temporary Business Manager (Grant & Consortium Operations

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