Postdoc Research Associate in Bioinformatics/Computational Biology

The laboratory of Chongzhi Zang at the Center for Public Health Genomics, University of Virginia (UVA) is seeking to fill multiple Postdoctoral Research Associate positions in the broad field of bioinformatics and computational biology. The research program in the lab focuses on developing computational methodologies and designing integrative data science approaches to study chromatin epigenomics and gene regulation. Current ongoing projects include: multi-omics integration-based algorithm development for transcriptional regulation prediction; model-based algorithm development for single-cell epigenomics and spatial multi-omics data analysis; statistical and computational modeling of phase-separated transcriptional condensation; global epigenetic and transcriptional regulation in T-cell immunity and various human cancer systems, etc. More information on research directions and previous publications can be found at the lab website : zanglab.org .

The Zang Lab is well funded by NIH and other agencies. Each postdoctoral scientist in the lab will receive comprehensive and personalized training in research and career development, and will have extensive opportunities for independent and collaborative research. Based at the Center for Public Health Genomics in UVA’s School of Medicine, the lab has established close collaborations with multiple labs both within and outside the university, including departments of Biochemistry and Molecular Genetics, Biomedical Engineering, Statistics, UVA Cancer Center and the new founded School of Data Science, as well as several other universities and research institutes.

Requirements

1. A Ph.D. or equivalent degree in any quantitative science, including but not limited to Bioinformatics, Computational Biology, Applied Mathematics, Statistics, Physics, Chemistry, Computer Science, Data Science, Engineering or a related field is required by the start date;

2. Proficient in Python (or C/C++) & R programming;

3. Excellent communication and teamwork skills;

4. Strong quantitative background (e.g., statistical modeling, machine learning, computational or theoretical physics, etc.) or computational genomics experience (e.g., high-throughput sequencing data analysis, etc.);

5. At least one peer-reviewed publication written in English in the previous area of research (not necessarily related to biology) with submitted, accepted or published status at the time of application.

The University of Virginia is an equal opportunity and affirmative action employer. Women, minorities, veterans and persons with disabilities are strongly encouraged to apply.

Application Process

All positions are restricted and contingent on the continuation of funding. Please direct any questions or inquiries to zang@virginia.edu . The positions will remain open until filled.

For further information regarding the application process, please contact: Rhiannon O'Coin at rmo2r@virginia.edu

To apply please visit UVA job board https://uva.wd1.myworkdayjobs.com/UVAJobs, and search for " R0040016 ".

Complete the application and see below for documents to attach.

Required Application Materials:

• CV
• Cover Letter
• Contact information for 3 references

Please note multiple documents can be submitted in the CV/Resume Box. Applications that do not contain all of the required documents will not receive full consideration.

The selected candidate will be required to complete a background check at time of offer per University Policy.

The University of Virginia, i ncluding the UVA Health System which represents the UVA Medical Center, Schools of Medicine and Nursing, UVA Physician’s Group and the Claude Moore Health Sciences Library, are fundamentally committed to the diversity of our faculty and staff. We believe diversity is excellence expressing itself through every person's perspectives and lived experiences. We are equal opportunity and affirmative action employers. All qualified applicants will receive consideration for employment without regard to age, color, disability, gender identity or expression, marital status, national or ethnic origin, political affiliation, race, religion, sex (including pregnancy), sexual orientation, veteran status, and family medical or genetic information.