Postdoctoral Research Associate

Postdoctoral Research Associate

We are looking for a highly motivated Postdoctoral Research Associate to join the Platig Lab at the University of Virginia. The candidate would be part of an interdisciplinary team of computational biologists, data scientists, and RNA biologists investigating the role of alternative splicing in Type 1 Diabetes (T1D). The project will leverage large-scale generation (400+ samples) of long-read RNA-seq from CD4+ T cells in a T1D cohort across multiple time points. A core aim of this project will be to develop interpretable machine learning approaches to understand how RNA binding proteins (RBPs) regulate observed splicing changes and to test putative mechanisms experimentally. This is a unique opportunity to work at the intersection of machine learning, RNA biology, and immunology, with translational relevance to T1D.

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. This is a 12-month appointment with the possibility of renewal contingent upon satisfactory performance and the availability of funding.

Responsibilities

• Integrate long-read and short-read RNA-seq with RBP data (motifs, eCLIP) for splicing and isoform analysis
• Develop machine learning methods to predict functional RNA regulatory elements
• Collaborate with wet-lab partners to design follow-up experiments
• Publish findings and present at national conferences
• Contribute to grant writing and mentorship of graduate students

Minimum Qualifications

• PhD (awarded or imminent) in bioinformatics, computational biology, or a closely related field
• Demonstrated experience using long- or short-read RNA-seq to understand alternative splicing
• Experience using machine learning techniques in genomics
• Strong publication record
• Ability to communicate computational techniques to a broad audience

Preferred Qualifications

• Experience modeling RNA binding proteins
• Familiarity with T1D
• Experience with machine learning interpretability approaches (xAI)

This is primarily a sedentary job involving extensive use of desktop computers. The job does occasionally require traveling some distance to attend meetings, and programs. Salary will be commensurate with education, experience, and NIH guidelines. This is an Exempt-level, benefitted position. For more information on the benefits available to postdoctoral associates at UVA, visit postdoc.virginia.edu and hr.virginia.edu/benefits.

Application review/deadline This position will remain open until filled. Background checks and pre-employment health screenings will be conducted on all new hires prior to employment. Please apply online through Online and search for R0083627. Complete the application and upload the following required materials: Cover letter, Resume. 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. 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 application process, please contact Bill Crane, View email address on click.appcast.io. For questions about the position, please contact Jennifer Dean, View email address on click.appcast.io. 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.