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NLM Division: Intramural Research Program (IRP)

NLM Program/Office: Computational Biology Branch (CBB)

Application Period: Applications will be accepted May 10 until the position is filled.

Overview: 

Seeking an outstanding candidate to conduct research on algorithms for Computational Biology, with a focus on gene regulation and cancer progression. This position is located at the NIH main campus in Bethesda, Maryland, U.S.A.

Position Description/Responsibilities:

The NLM IRP is seeking a Staff Scientist 1 with demonstrated expertise in developing bioinformatics methods to join Dr. Teresa Przytycka's research group. Key responsibilities include:

• Develop novel advanced optimization and/or machine learning approaches to study gene regulation, cancer mutation landscapes, and single-cell analysis.
• Utilize large-scale datasets to enhance understanding of cellular mechanisms in health and disease, as well as their evolution.
• Provide computational support to the research group in data processing and analysis.
• 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 postbaccalaureate fellows to foster their professional development.
• Stay current with advances in computational biology methods and algorithms, including machine learning and optimization approaches, as well as new experimental techniques relevant to the field.

Position Requirements:

The ideal candidate may or may not be a United States citizen but must hold a doctoral degree. We are seeking an individual with:

• Four or more years of relevant postdoctoral experience, with a strong publication record demonstrating significant contributions to research through peer-reviewed publications.
• Proven ability to apply mathematical modeling across a wide range of challenges.
• Expertise in developing innovative computational algorithms applicable to cancer biology, network biology, and/or gene regulation, as evidenced by first-author peer-reviewed publications in the specified research areas.
• Experience in working with Next Generation Sequencing (NGS) and emerging technologies such as single-cell data.
• Demonstrated ability to work both independently and as a team member. 

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‐time position. 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 experience in developing optimization techniques, combinatorial algorithms, and/or machine learning along with the names of three references, to nlmirpjobs@mail.nih.gov. Please ensure to include the announcement number, NLM9808-2024, 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.

Contact Information: nlmirpjobs@mail.nih.gov

HHS, NIH, and NLM are equal opportunity employers.

Last Reviewed: May 10, 2024