The Zhang Lab in the Department of Biochemistry and Molecular Genetics at University of Virginia School of Medicine is seeking a highly motivated, organized and enthusiastic postdoctoral research associate for high-throughput proteomic studies. The Zhang lab focuses on the development and application of mass spectrometry-based methods for identification of key players in cancer biology and drug discovery. We integrate high-throughput proteomic, genetic, chemical, bioinformatics tools to dissect the regulatory mechanisms involved in cancer development and treatment. 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. What you will do: * Full-time research or scholarship at the University. * Preparatory for a full-time academic or research career * Supervised by a senior scholar, and allows the appointee to publish the results of his/her research or scholarship Accurate recording and collecting of data collection * Presenting data in regularly scheduled lab meetings * In addition to the above job responsibilities, other duties may be assigned. Qualifications: * PhD degree in Pharmacology, Chemistry, Biochemistry, Molecular Biology, or a related Biomedical Science discipline by the start date of the appointment * Prior experience with HPLC, and with MS, LC-MS, and/or LC-MS/MS is preferred. * Prior experience with bioinformatics analysis is preferred. * Prior experience with cell culture and sample preparation is preferred. This is a 12-month appointment with the possibility of renewal in one-year increments, contingent upon satisfactory performance and the availability of funding. The selected applicant will be required to complete a background check and health assessment per University policy. To Apply: Please visit UVA job board: https://uva.wd1.myworkdayjobs.com/UVAJobs and search for "R0051457" Complete an application online and attach the following: * Cover letter summarizing your pass experience, research interests, and career goals. * CV/Resume. * Contact information for 3 references PROCESS FOR INTERNAL UVA APPLICANTS: Please apply through your Workday Home page, search "Find Jobs", and search for "R0051457" Review of applicants will begin August 18, 2023 and the position will remain open until filled. * Please note that you MUST upload ALL documents into the CV/Resume box. Applications that do not contain all of the required documents will not receive full consideration. * For questions about the application process, please contact Rhiannon O'Coin, Senior Academic Recruiter, rmo2r@virginia.edu For more information about UVA and the Charlottesville community please see www.virginia.edu/life/charlottesville and https://embarkcva.com/ PHYSICAL DEMANDS 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. The University of Virginia, including 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, pregnancy, sexual orientation, veteran or military status, and family medical or genetic information.