The Visual Intelligence Laboratory ('Baek Lab') at the University of Virginia School of Data Science is seeking a motivated and self-driven postdoctoral researcher for pioneering research at the convergence of artificial intelligence (AI) and physics. The ideal candidate should possess expertise in physics-informed/constrained machine learning-topics including, but not limited to: * Physics-informed neural networks (PINN) & neural operators * Physics-aware convolutional neural networks (PARC) * Meta-learning/transfer learning, small data learning * Active learning, Bayesian deep learning, uncertainty quantification This position involves active participation in a well-funded research program sponsored by federal agencies such as the U.S. Department of Defense and the National Science Foundation (NSF). The focus of the research is on developing innovative physics-informed deep learning algorithms to model, characterize, predict, and design the physical behaviors of diverse mechanical systems. Responsibilities also include the development of software codes, dissemination of research through publications, and mentoring PhD students. The lab's extensive collaborative network, both nationally and internationally, spans various disciplines, offering diverse applications across physics, mechanical engineering, materials sciences, and more. This multidisciplinary and highly collaborative environment provides an exceptional opportunity for candidates to thrive and become sought-after experts in scientific machine learning (SciML), with numerous opportunities in academic, government, and industry positions. The position is offered for one year, continuation is contingent upon funding and satisfactory performance. Minimum Qualifications * Effective written and verbal communication skills. * PhD degree in mechanical engineering, materials science, physics, computer science, or related fields, to be completed by the start date of the appointment. * Robust mathematical proficiency, with a focus on subjects including linear algebra, vector calculus, optimization, and statistics/probability. * Proficient computer programming skills in languages such as C/C++ and Python. TO APPLY: Please visit https://jobs.virginia.edu and search for R0063329 Complete an application online and attach: * Cover Letter detailing your experience, qualifications, and interest in this position * Up-to-date CV * A summary of your most significant research accomplishments (up to 500 words) * Names and contact information of three references Internal Applicants: Please apply through your Workday Home page, search "Find Jobs", and search for "R0063329" * 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 The selected applicant will be required to complete a background check prior to their first day of employment per University policy. Interested candidates are encouraged to contact Prof. Stephen Baek ( baek@virginia.edu ) for more info. The Visual Intelligence Laboratory is committed to equal student employment opportunity regardless of race, ethnicity, color, national origin, religion, sex, gender identity, age, marital status, disability, or other factors that are irrelevant to the advertised position. If you have a special need for the interview, please do not hesitate to indicate required accommodations. 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, pregnancy, sexual orientation, veteran or military status, and family medical or genetic information.