Post-Doctoral Fellow (Genetic Epidemiology and Bioinformatics)

Much remains unknown about how genetic risk contributes to each allergic phenotype, and even less is known on how it can lead to cumulative risk across phenotypes of the atopic march. Evidence suggests that there may be greater accuracy to predict risk and severity for a single allergic outcome when genetic risk prediction is built including evidence from multiple atopic march outcomes. Only a multi-omics approach can reveal signatures for disease, endotypes and severity trajectories with a clinical translation value. The Genomics and Precision Health Section (GPHS) does extensive work on (i) genetics across the atopic march; (ii) integrative-omics and systems biology across the atopic march; (iii) examining genetics beyond germline variation considering the importance of the dynamic genome; and (iv) dissemination of data and resources to the scientific community.

Successful candidates will work at the intersection of epidemiology, statistical genetics, integrative -omics, and bioinformatics by leveraging existing population cohorts, extensive data on genetics, transcriptomics, proteomics and DNA methylation and state-of-the-art informatics platforms to address our research gaps. The fellow will have the opportunity to lead individual and collaborative projects aimed at identifying signatures of disease and endotypes, response to intervention, disease mechanisms, and integrative models of disease risk with the goal of clinical translation. They will also have the opportunity to interact with numerous national and international consortium-level efforts.

Key Responsibilities Include:

• Designing studies and data analyses.
• Conducting analyses integrating multi-omics and clinical phenotypes.
• Executing statistical and bioinformatic workflows for large-scale datasets.
• Collaborating with world-class epidemiologists, clinicians, bioinformaticians, and external partners.
• Preparing manuscripts and present findings at national and international scientific meetings.

Benefits Include:

• A highly collaborative research environment within the NIH Intramural Research Program and extramural networking opportunities through collaborations and consortium efforts.
• Access to exceptional resources, technologies, biorepositories, and cohorts.
• Competitive stipend and benefits (https://www.training.nih.gov/stipends/). Potential +$10,000 annual stipend bonus for those with specialty skills explicitly noted on terminal degree diploma (e.g., epidemiology, biostatistics, etc.).
• Limited relocation assistance
• Opportunities for career development, training, and mentorship tailored to your goals.

This position is based on the NIH main campus in Bethesda, Maryland. Candidates are expected live or relocate to within a 50-mile radius of the NIH campus and work on-site. There is some flexibility for ad hoc telework per NIH/HHS policy.

• Ph.D. in Genetic Epidemiology/Statistical Genetics/Bioinformatics or a closely related field with experience in analysis and interpretation of high-throughput omics data (Applicants close to finishing their PhDs in these areas will be considered)
• Demonstrated experience analyzing multi-omics data.
• Strong knowledge and experience in programming languages such as R, Python, Bash and handling large-scale computational tasks/datasets.
• Experience with high compute cluster environments, network-based analyses, and/or polygenic risk scores.
• An interest in applying computational methods to solve complex biological problems.
• Excellent communication skills, both written and verbal, as evident through publications and presentations.
• Ability to work both independently and in a collaborative manner with a diverse group of scientists and clinicians.

U.S. citizens and permanent residents are eligible to apply. NIH welcomes foreign nationals with the exception of individuals from this list: https://www.state.gov/countries-of-particular-concern-special-watch-list-countries-entities-of-particular-concern/