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STAFF SCIENTIST 1 - Human Regulatory Genomics

National Institutes of Health/National Library of Medicine
Bethesda, Maryland
Salary will be commensurate with experience and qualifications.
Closing date
Aug 1, 2024

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Life Sciences, Bioinformatics
Position Type
Full Time
Job Type
Staff Scientist
Organization Type

Job Details

STAFF SCIENTIST 1 - Human Regulatory Genomics

Position Information:
The National Library of Medicine (NLM) is one of 27 Institutes and Centers at the National Institutes of Health. NLM is a global leader in biomedical informatics and computational health data science and the world’s largest biomedical library. NLM’s legislative mandate is to support the essential work of acquiring, organizing, preserving, and disseminating biomedical information. NLM provides public access to this information 24 hours a day, seven days a week.

NLM Division:    Intramural Research Program (IRP)

NLM Program/Office:   Computational Biology Branch (CBB)

Application Period:    Applications will be accepted May 14, 2024, until the position is filled.

Overview:     Seeking an exceptional candidate to conduct research in human genomics, develop novel machine learning methodologies, and create programs addressing critical questions in gene regulation under the guidance of Dr. Ivan Ovcharenko. This position is located at the NIH main campus in Bethesda, Maryland, U.S.A.

Position Description/Responsibilities:
We are seeking a highly motivated individual to join our team specializing in human genomics and machine learning. In this role, you will be responsible for conducting cutting-edge research, devising innovative machine learning methodologies, and developing programs aimed at addressing critical questions in gene regulation.

Key Responsibilities:
• Conduct research in human genomics to advance understanding of gene regulation mechanisms.
• Develop novel machine learning techniques tailored for applications in human regulatory genomics.
• Utilize large-scale epigenetic and functional genomic datasets, genome-wide association studies, and population genetics data to construct predictive models of gene regulatory elements.
• Design and implement AI models to accurately identify disease-causing mutations in complex human diseases.
• Publish research findings in high-impact, peer-reviewed journals, and present results at national and international conferences.
• Collaborate with other NIH institutes and external research organizations to leverage complementary expertise and resources.
• Mentor postdoctoral and post-baccalaureate fellows to foster their professional development.
• Stay abreast of advances in AI, computational biology, and experimental techniques relevant to the field.

The ideal candidate may or may not be a United States citizen.

We are looking for an individual with:
• Three or more years of pertinent postdoctoral experience with a robust publication record demonstrating significant contributions to research through peer-reviewed publications.
• Expertise in regulatory genomics, enhancer/silencer identification, disease genetics, population genetics, and/or functional genomics, coupled with experience and/or contemporary understanding of eukaryotic gene regulation principles.
• Proficiency in handling ENCODE, NIH Roadmap Epigenomics, and analogous datasets, along with demonstrated proficiency in classical machine learning and deep learning algorithms.
• Proven capability to apply mathematical modeling across a wide spectrum of challenges and fluency in Python and R, including proficiency in Tensorflow and PyTorch libraries, with familiarity with GPU-based computational architectures.
• Demonstrated ability to collaborate effectively on interdisciplinary projects, coupled with experience in mentoring and strong verbal and written communication skills.

Education Requirements:

Candidates must hold a doctoral degree in a quantitative field, such as Computational Biology, Computer Science, Bioinformatics or Mathematics, or related field.

Foreign Education: Applicants who have completed part or all their education outside of the United States must have their foreign education evaluated by an accredited organization to ensure that the foreign education is equivalent to education received in accredited educational institutions in the United States. We will only accept the completed foreign education evaluation. For more information on foreign education verification, visit the National Association of Credential Evaluation Services (NACES) website.

Verification must be received prior to the effective date of the appointment.

Salary and Benefits:
This non-competitive appointment in the excepted service is like a federal full-􀆟me posi􀆟on. Salary will be commensurate with experience and qualifications. A full package of benefits is available.

How To Apply:
Prospective candidates are encouraged to submit their CV and Bibliography, accompanied by a cover letter detailing their research interests and proficiency in AI developments, along with the names of three references, to Please ensure to include the announcement number, NLM9491-2024, in the cover letter. Please refrain from including personal information such as birth date, social security number (SSN), or personal photograph in your application materials.

DHHS, NIH, and NLM are Equal Opportunity Employers


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