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The Department of Biomedical Engineering at the University of Virginia seeks a highly motivated postdoctoral Research Associate to lead multidisciplinary systems biology projects in the laboratory of Dr. Sepideh Dolatshahi. The Dolatshahi Human Systems Immunology laboratory is active primarily at the interface of systems biology, immunology, and bioengineering. Research in the lab is built on our multidisciplinary experience in combining integrative computational immunology - data-driven, state-of-the-art single cell resolution and spatial methods, machine learning and kinetic modeling - with integrative experimental immunology to understand the fundamental mechanisms of immune regulation and dysregulation, with an ultimate objective of identifying strategies to improve immunotherapies and vaccines. The project will focus on studying immunology of pregnancy and immunity at the fetal-maternal interface. Research in the Dolatshahi lab benefits from proximity to the UVA hospital and active collaboration with leaders in Carter Immunology Center and the division of Fetal-Maternal Medicine at UVA. Candidates with strong background in molecular and cellular biology/immunology (or a related discipline) and interest in learning computational methods are particularly encouraged to apply. Candidates with bioengineering or systems biology background and extensive wet-lab experience are also strongly encouraged to apply. For more information visit Dolatshahi lab homepage at: https://www.dolatshahilab.com/ .
Postdoctoral employment is temporary and is normally limited to an individual who has been awarded a Ph.D. or equivalent doctorate within the previous five years and who will be involved in full-time research or scholarship at the University. Employment as a Postdoctoral Research Associate is viewed as training and is preparatory for a full-time academic or research career, is supervised by a senior scholar, and allows the appointee to publish the results of his/her research or scholarship during the training period.
Candidates with strong background in molecular and cellular immunology (or a related discipline), molecular biology techniques, and interest in learning computational methods are particularly encouraged to apply. Candidates with bioengineering or systems biology background and wet-lab experience are also strongly encouraged to apply. The following qualifications are required : deep understanding of and coursework in molecular and cellular biology, immunology, and statistical and inferential analysis of data; strong oral and written communication skills with a strong publication record and grant report experience in relevant fields; proficiency in performing literature search/review; the capability to work effectively with graduate and undergraduate students, as well as colleagues from multiple disciplines; proficiency in computer programming such as MATLAB, R, or Python (this requirement could be changed from proficiency to familiarity if the suite of experimental skills are specifically strong and relevant; our lab can provide training to strengthen the computational proficiency). The following experience is preferred : broad biological knowledge in pregnancy and fetal-maternal medicine and immunity; experience with IRB-approved human cohort studies and placental tissue; the ability to gather, organize, and synthesize complex information to draw model assumptions and parameterization strategies for dynamic models; familiarity with variability across the human population as related to public health and social determinants of health; and familiarity with multiplex immunohistochemistry, spatial proteomics and transcriptomics.
Minimum Qualifications

• Candidates must have a Ph.D. degree in Immunology, Computational/Systems Biology, Biomedical Engineering, or a closely related discipline.
• The successful applicant will have a tangible track record as assessed by first author publications, awards or fellowships.

Physical Demands
This job involves a combination of experimental lab work as well as computational sedentary work involving extensive use of desktop computers. The job does also occasionally require traveling some distance to attend meetings, and programs.
The anticipated hiring range is $62,000-$68,000, commensurate with education and experience.
This is an exempt-level, benefited position. Learn more about UVA benefits .
This is a restricted position, which is dependent on funding and is contingent upon funding availability.
This position is based in Charlottesville, VA, and must be performed fully on-site.
To learn more about UVA and in the Charlottesville area, visit UVA Life and Embark CVA .
Application review will begin afterOctober 4, 2025.
Background checks and pre-employment health screenings will be conducted on all new hires prior to employment.
This position will not consider candidates who require immigration sponsorship now or in the future.
How to Apply .
Please apply online , by searching for requisition number R0076604. Complete an application with the following documents:

• Resume
• Cover Letter

Upload all materials into the resume submission field. You can submit multiple documents into this one field or combine them into one PDF. Applications without all required documents will not receive full consideration.
Internal applicants: Search and apply for jobs on the UVA Internal Careers website .
Reference checks will be completed by UVA's third-party partner, SkillSurvey, during the final phase of the interview. Five references will be requested, with at least three responses required.
For questions about the position, please contact Sepideh Dolatshahi, sd8us@virginia.edu .
For questions about the application process, please contact Jessica Russo, Senior Recruiter, sxv9zv@virginia.edu.
Job Profile
J0267 - Research Associate - 12 Month
Career Stream and Level
No Career Stream-No Level
The University of Virginia is an equal opportunity employer. All interested persons are encouraged to apply, including veterans and individuals with disabilities. Learn more about UVA's commitment to non-discrimination and equal opportunity employment .