Postdoctoral Associate

Job Description The ASTRO Lab in the Department of Mechanical Engineering at Virginia Tech seeks applications for a Postdoctoral Associate to conduct research in uncertainty quantification and data-driven modeling as part of a multidisciplinary research program. The successful candidate will develop and implement uncertainty quantification, Bayesian inference, multi-fidelity, and reduced-order modeling methods, integrate these methods with finite element and machine-learning models, contribute to model validation and predictive modeling efforts, and work collaboratively with faculty, postdoctoral researchers, and graduate and undergraduate students. The candidate will also contribute to the preparation of peer-reviewed publications and scientific presentations and will assist in mentoring students involved in related research activities. Required Qualifications Ph.D. in Mechanical Engineering, Aerospace Engineering, Engineering Mechanics, Computational Science, Applied Mathematics, or a closely related field. PhD must be awarded no more than four years prior to the effective date of appointment with a minimum of one year eligibility remaining. Demonstrated experience in uncertainty quantification, Bayesian inference, or computational modeling of physical or engineering systems. Experience with scientific programming using Python, MATLAB, C/C++, or similar computational tools. Demonstrated record of scholarly research, including peer-reviewed publications. Preferred Qualifications Strong scientific writing skills and ability to communicate research findings clearly and effectively. Strong oral presentation skills, with experience presenting research to scientific or technical audiences. Ability to work independently, take ownership of research activities, and contribute effectively to collaborative research. Ability to work effectively with undergraduate and graduate students and contribute to their research training and mentoring. Strong organizational, analytical, and interpersonal skills. Experience in one or more of the following areas: multi-fidelity or surrogate modeling, sensitivity and identifiability analysis, reduced-order modeling, machine learning for scientific computing, finite element analysis, or constitutive modeling. Experience developing and validating computational models. Experience with model calibration under sparse or heterogeneous data. Experience preparing manuscripts for publication in peer-reviewed journals. Experience presenting research at scientific conferences or professional meetings. Previous experience mentoring or supervising undergraduate or graduate researchers. Demonstrated ability to collaborate effectively across disciplines. Overtime Status Exempt: Not eligible for overtime Appointment Type Restricted Hours per week 40 Review Date October 1, 2026 Additional Information The successful candidate will be required to have a criminal conviction check. About Virginia Tech Dedicated to its motto, Ut Prosim (That I May Serve), Virginia Tech pushes the boundaries of knowledge by taking a hands-on, transdisciplinary approach to preparing scholars to be leaders and problem-solvers. A comprehensive land-grant institution that enhances the quality of life in Virginia and throughout the world, Virginia Tech is an inclusive community dedicated to knowledge, discovery, and creativity. The university offers more than 280 majors to a diverse enrollment of more than 36,000 undergraduate, graduate, and professional students in eight undergraduate colleges, a school of medicine, a veterinary medicine college, Graduate School, and Honors College. The university has a significant presence across Virginia, including Blacksburg, the greater Washington, D.C. area, the Health Sciences and Technology Campus in Roanoke, sites in Newport News and Richmond, and numerous Extension offices and research institutes. A leading global research institution, Virginia Tech conducts more than $650 million in research annually. Virginia Tech endorses and encourages participation in professional development opportunities and university shared governance. These valuable contributions to university shared governance provide important representation and perspective, along with opportunities for unique and impactful professional development. 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