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Postdoctoral Associate in Uncertainty Quantification and Data-Driven Modeling at Virginia Tech Virginia Tech in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Available
Country
United States
University
Virginia Tech
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About this position
Virginia Tech’s ASTRO Lab in the Department of Mechanical Engineering is recruiting a Postdoctoral Associate for research in uncertainty quantification and data-driven modeling. The position sits within a multidisciplinary research program and focuses on developing and implementing methods such as Bayesian inference, multi-fidelity modeling, and reduced-order modeling, then integrating them with finite element and machine-learning models for validation and predictive modeling.
The successful candidate will work closely with faculty, postdoctoral researchers, and graduate and undergraduate students, contribute to peer-reviewed publications and scientific presentations, and help mentor students involved in related research activities.
Research areas / keywords: uncertainty quantification, Bayesian inference, computational modeling, multi-fidelity and surrogate modeling, reduced-order modeling, sensitivity and identifiability analysis, machine learning for scientific computing, finite element analysis, constitutive modeling, model calibration.
Eligibility highlights: Applicants must hold a Ph.D. in Mechanical Engineering, Aerospace Engineering, Engineering Mechanics, Computational Science, Applied Mathematics, or a closely related field. The Ph.D. must have been awarded no more than four years before the effective date of appointment, with at least one year of eligibility remaining. Required experience includes uncertainty quantification, Bayesian inference, or computational modeling of physical/engineering systems, plus scientific programming in Python, MATLAB, C/C++, or similar tools, and a scholarly publication record.
Preferred qualifications: strong scientific writing and oral presentation skills, ability to work independently and collaboratively, mentoring experience, strong organizational and interpersonal skills, and experience with model development/validation, sparse or heterogeneous data, and manuscript preparation.
Location: Blacksburg, Virginia, United States.
How to apply: Submit an application through the Virginia Tech jobs portal using the Apply Now button on the posting page. Review date listed on the posting is October 1, 2026.
Funding details
Available
What's required
Ph.D. in Mechanical Engineering, Aerospace Engineering, Engineering Mechanics, Computational Science, Applied Mathematics, or a closely related field. The PhD must have been awarded no more than four years prior to the effective date of appointment, with at least one year of eligibility remaining. Applicants should have demonstrated experience in uncertainty quantification, Bayesian inference, or computational modeling of physical or engineering systems, experience with scientific programming in Python, MATLAB, C/C++, or similar tools, and a record of scholarly research including peer-reviewed publications. Preferred qualifications include strong scientific writing and oral presentation skills, ability to work independently and collaboratively, mentoring experience, and experience with multi-fidelity/surrogate modeling, sensitivity and identifiability analysis, reduced-order modeling, machine learning for scientific computing, finite element analysis, constitutive modeling, model calibration under sparse or heterogeneous data, and manuscript preparation.
How to apply
Apply through the Virginia Tech jobs portal using the Apply Now button on the posting page. Prepare to submit the required application materials through the online form.
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