Selected NIH Intramural Research and other job openings-February 2020

[Richard Schugart]

from: Owens, Roland (NIH/OD) [E] <owensrol@mail.nih.gov>
reply-to: Mejia, Raymond (NIH/NHLBI) [E] <ray.mejia@nih.gov>

Staff Scientist / Computational Biologist
Epigenetics & Stem Cell Biology Laboratory, NIEHS
(review begins: February 28)

The Epigenetics & Stem Cell Biology Laboratory of the National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), Research Triangle Park, North Carolina is recruiting for a Staff Scientist for the Systems Biology Group under the leadership of Principal Investigator Dr. Raja Jothi. The Jothi Lab uses pluripotent embryonic stem cells as a model system to understand transcriptional and epigenetic control of cell fate decisions during development, differentiation, and homeostasis.

A Staff Scientist is a NIH employee generally appointed to a time-limited, renewable position to support the long-term research of a Principal Investigator. Staff Scientists do not receive independent resources, although they often work independently and have sophisticated skills and knowledge essential to the work of the laboratory. Staff Scientists should be capable of independently designing experiments, but do not have responsibilities for initiating new research programs. The Staff Scientist in this position will conduct independent research and mentor postdoctoral fellows and other laboratory trainees.

We are seeking an experienced bioinformatics/computational biologist with a proven track record in algorithm/methods development in the area of proteogenomics. Candidates are required to have extensive experience analyzing high-throughput data generated using (A) mass spectrometry (MS)-based proteomics technology and (B) next-generation sequencing platforms (ChIP-Seq, RNA-Seq, etc.). Successful candidates will have experience working in the areas of embryonic stem cells, transcriptional regulation, chromatin biology, and epigenetic mechanisms. Outstanding interpersonal skills, evidence of teamwork and mentoring, and excellent oral and written communication skills are desired.

Qualifications: Candidates must possess a Ph.D. in Computer Science, Bioinformatics, or related discipline, 4+ years of postdoctoral experience, an outstanding publication record (in the areas of proteogenomics, transcription, and epigenetics), and evidence of mentoring responsibilities.

Salary/benefits: This is a federal, full-time equivalent position, and a comprehensive benefits package is available. Salary will be commensurate with experience and qualifications.

How to apply: Interested persons should email one combined PDF of their curriculum vitae, bibliography, and a two-page statement of research background and interests to int-appls@niehs.nih.gov. Include your name and Vacancy Announcement NR117 in the subject line. Applicants must also arrange to have 3 letters of reference sent directly to int-appls@niehs.nih.gov. Referees should include your name and Vacancy Announcement NR117 in the subject line of their email. Incomplete application packages will not be accepted or reviewed. Review of applications will begin on February 28, 2020.

The NIH is dedicated to building a diverse community in its training and employment programs. DHHS and NIH are Equal Opportunity Employers. Applications from women, minorities, and persons with disabilities are strongly encouraged.

Tenure-Track/Tenure-Eligible Investigator
Biostatistics Branch, NCI-DCEG
(review begins: February 29)

The Biostatistics Branch (BB) in the Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute (NCI), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), is recruiting for a tenure-track/tenure eligible position to work on methods development and applications in a highly collaborative and data-rich environment with one-of-a-kind data and computational resources.

BB investigators develop novel analytical approaches from cutting-edge scientific challenges faced by epidemiological studies to identify and characterize environmental and genetic determinants of cancer risk, as well as clinical and cancer prevention studies. These challenges include choosing an efficient study and sampling design, integrative analyses of high-dimensional, time-dependent data such as data derived from electronic medical records, geographical/spatial statistics, biosensors, omic technologies to measure biomarkers, genomics, as well as designing validation studies and methods to evaluate and correct for measurement error in exposures and clinical outcomes.

The successful applicant will have opportunities to work on a wide range of analytical challenges because of the breadth and depth of our program. They will develop an independently initiated methodological research program that will focus on solving statistical challenges in cancer epidemiology and/or genetics. Opportunities to hire postdoctoral fellows and computational support will be provided. Of particular interest are applicants focused on methodological and applications research in high dimensional and integrative data, spatial data analysis, and causal inference. We seek qualified applicants with all areas of statistical expertise in methods, including but not restricted to semiparametric and survival analysis including competing risks, functional data analyses, Bayesian and non-Bayesian computations, and network theory.

Applications will be evaluated on demonstrated potential to develop a creative, independent program of statistical research applicable to cancer epidemiology and genetics, and to collaborate effectively on epidemiologic studies. The ideal candidate will have the opportunity to build an innovative research program that takes advantage of the highly collaborative and data-rich environment of DCEG. Applicants should have a doctorate in biostatistics, statistics or a related field and knowledge of the basic approaches used in cancer epidemiology. A record of publications demonstrating an ability to conduct independent research on statistical methods is required. Publications documenting collaborative research in epidemiologic, clinical, biomedical or biological sciences are highly desirable. The successful candidate should have strong communication skills to discuss scientific issues with non-statistician colleagues and to write scientific papers.

Selection for this position will be based solely on merit, with no discrimination for non-merit reasons such as race, color, religion, gender, sexual orientation, national origin, political affiliation, marital status, disability, age, or membership or non-membership in an employee organization. NIH encourages the application and nomination of qualified women, minorities and individuals with disabilities. NIH provides reasonable accommodations to applicants with disabilities. If you require reasonable accommodation during any part of the application and hiring process, please notify us. The decision on granting reasonable accommodation will be made on a case-by-case basis. This position is subject to a background investigation. Salary is commensurate with research experience.

Interested individuals should send a cover letter; curriculum vitae and bibliography; please include in your CV a description of your mentoring and outreach activities, especially those involving women and persons from racial/ethnic or other groups that are underrepresented in biomedical research, a brief summary of research experience, accomplishments and research interests and goals; copies of three publications or preprints; and three letters of reference to:

Ms. Linda Littlejohn
Division of Cancer Epidemiology and Genetics, National Cancer Institute
9609 Medical Center Drive, Suite 7E328, MSC 9775
Bethesda, MD 20892-9775
Email: NCIDCEGOMR@mail.nih.gov

The review of applications will begin on or about February 29, 2020, but applications will be accepted until the position is filled. Please contact Dr. Paul Albert (phone 240-276-7593 or albertp@mail.nih.gov) for questions about the position. HHS, NIH, and NCI are equal opportunity employers.

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