Department of Health and Human Services
National Institutes of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Biostatistics and Computational Biology Branch
Division of Intramural Research
Research Triangle Park, North Carolina
The Biostatistics and Computational Biology Branch in the Division of Intramural Research of the National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), Research Triangle Park, North Carolina is recruiting two Staff Scientists, one to support the research activity in the Applied Statistics Group led by Shanshan Zhao, Ph.D., and one to support the Signal Processing group led by Leping Li, Ph.D..
A Staff Scientist 1 is an NIH employee generally appointed to a time-limited, renewable position, similar to that of a non-tenure track research faculty at many universities. Staff Scientists do not receive independent resources, but often work independently and have sophisticated skills and knowledge essential to the work of the group. Staff Scientists should be capable of independently designing studies, but do not have responsibilities for initiating new research programs. Appointees may be U.S. citizens, resident aliens, or non-resident aliens with, or eligible to obtain, a valid employment-authorization visa.
Staff Scientist Position 1 – Applied Statistics Group (NR144-ASG)
The group employs a dynamic approach by integrating statistical methodological developments and collaborative population-based studies to reveal the social and physical determinants for health outcomes, with a focus on time-to-event data in cancer and environmental epidemiological studies.
The primary responsibilities of the successful candidate will be to: 1) Lead scientific activities in developing powerful methods for time-to-event data in clinical and epidemiological studies; 2) Provide statistical support on new and established projects in the group; 3) Serve as a resource for knowledge of statistical methods developed by the group for collaborators and trainees.
Qualifications for Position 1:
The successful candidate will have a strong background in statistical theory and computational skills. They need to be skilled in oral and written communication, statistical programming with R and have proven experience working productively with multidisciplinary teams. Experience with time-to-event outcomes, longitudinal data, cancer and environmental studies is desirable. Candidates should have a doctoral degree in biostatistics, statistics, or a closely related field with demonstrated biostatistical experience. For additional information, contact Shanshan Zhao, Ph.D., Principal Investigator, Biostatistics and Computational Biology Branch.
Staff Scientist Position 2 – Signal Processing Group (NR144-SPG)
The goal of the Signal Processing Group is to analyze a large-scale human sleep polysomnography data to gain the understanding of how sex and age affect sleep disordered breathing and health. The lab is collaborating with physician scientists who have generated a large amount of human sleep polysomnography data (for over 10,000 subjects), including electroencephalogram (EEG), electromyography (EMG), electrocardiography (ECG), and other types of sleep data. This data also contains clinical outcomes. The group has assembled a multidisciplinary team including physicians, statisticians, computer scientist/electrical engineers, and bioinformaticians with expertise in deep learning, signal processing, classification, and statistics. Ongoing projects in the lab include brain wave characterization, prediction of sleep stages and disease classification using machine learning approaches including deep learning.
The successful candidate will work with Leping Li, Ph.D. to support research activities and training in the field of computational biology.
The primary responsibilities of the successful candidate will be to:
- Serve as a resource for knowledge of advanced digital signal processing in EEG, speech, acoustics and/or deep learning for current and new members of the research group.
- Lead scientific activities pertaining to EEG signal processing and analysis, pattern recognition, and classification.
- Mentor postbaccalaureate fellows, medical fellows, and summer students.
Qualifications for Position 2:
Candidates should have a doctoral degree in electrical and computer engineering, computer science, or a related field and with at least 2 years of research experience in digital signal processing. The candidate must demonstrate strong quantitative skills including mastery of multiple programming languages including python, Matlab, and C/C++. Demonstrated experience with applications related to EEG, speech, acoustics, and deep learning is preferred. Mentoring experience and experience working in collaborative environments is desired but not required. For additional information, contact Leping Li, Ph.D., Principal Investigator, Biostatistics and Computational Biology Branch, at 984-287-3836.
The successful candidate for this position will be appointed at a salary commensurate with experience and accomplishments with full federal benefits, including leave, health and life insurance, retirement, and savings plan (401k equivalent).
How to apply:
Interested candidates must submit materials as one combined PDF via email to Bonnie Earnhardt at email@example.com. All emails should include vacancy number NR144-ASG or NR144-SPG in the subject line. A complete application includes:
- Cover letter: Please indicate which group you are applying to, including a description of relevant research experience.
- Curriculum Vitae with full Bibliography.
- Research Statement: A two-page statement describing your research and collaboration experience.
- DEIA Statement: Include a statement, not to exceed one page, which addresses your commitment to Diversity, Equity, Inclusion, and Accessibility.
In addition, you must also arrange for three letters of reference to be sent directly to firstname.lastname@example.org Letters should be on official letterhead, when possible, signed, and dated. Referees must include the applicant’s name and vacancy number NR144 in the email subject line.
Incomplete applications or paper applications will not be reviewed.
Questions about this position can be forwarded to Raja Jothi, Ph.D., Search Committee Chair. Review of applications will begin on May 30, 2023. The search will be closed once the position is filled.
Commitment to Diversity and Equal Employment Opportunity:
The NIH/NIEHS encourages the application and nomination of qualified women, minorities, and individuals with disabilities. The United States Government does not discriminate in employment on the basis of race, color, religion, sex (including pregnancy and gender identity), national origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non-merit factors. NIH/NIEHS will provide reasonable accommodations to applicants with disabilities as appropriate. If you require reasonable accommodation during any part of the application and hiring process, please notify us.
Equal Opportunity Employment:
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. The NIH encourages the application and nomination of qualified women, minorities, and individuals with disabilities.
Applicants who have completed part or all their education outside of the US 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). Verification must be received prior to the effective date of the appointment.
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