Postdoctoral Associate, Intelligent Systems Division

Commonwealth of VA Careers; Blacksburg (VA); On-site; USD 59,000 - 72,000; Full time

Job summary

The Virginia Tech National Security Institute (VTNSI) seeks applications for a Postdoctoral Associate to conduct research in computational modeling and uncertainty quantification as part of a multidisciplinary program.

The successful candidate will help develop surrogate models of computational systems via reduced-order and machine learning approaches and leverage Bayesian methods to estimate parameters, quantify uncertainty, and identify training needs to improve models.

Qualifications

• PhD in Applied Mathematics, Computational Science, Mechanical Engineering, Aerospace Engineering, Engineering Mechanics, or related field, awarded within last 4 years.
• US Citizenship due to project sensitivity.
• Experience with Bayesian inference and uncertainty quantification.
• Experience with computational modeling of physical or engineering systems.
• Programming experience in Python, MATLAB, Julia, C/C++, or similar.

Responsibilities

• Develop surrogate models of computational systems using reduced-order and machine learning methods.
• Apply Bayesian methods to estimate parameters and quantify uncertainty.
• Identify training points for model evaluation to improve accuracy.
• Publish peer‑reviewed work and prepare scientific presentations.
• Mentor students involved in related research and collaborate across institutions.

Skills

Bayesian inference

Parameter estimation

Uncertainty quantification

Computational modeling

Scientific programming

Education

PhD in Applied Mathematics or related field

Tools

Python

MATLAB

Julia

C/C++

Job description

Job Description

The Virginia Tech National Security Institute (VTNSI) seeks applications for a Postdoctoral Associate to conduct research in computational modeling and uncertainty quantification as part of a multidisciplinary research program. The successful candidate will help develop surrogate models of computational systems via both reduced-order and machine learning approaches and leverage Bayesian and other methods to estimate parameters, quantify uncertainty, and recommend new points for training or full-order model evaluation to improve models. 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. There will also be opportunities for travel to present in conferences and to participate in collaborations.

The position will contribute to a multi-institution, National Institutes of Health (NIH)- funded research project to better understand and predict women’s health outcomes by investigating how hormones shape the structure and function of vaginal tissue across the lifespan. The successful candidate will have the opportunity to collaborate with researchers in Mathematics and Mechanical Engineering at Virginia Tech as well as from several other institutions.

Required Qualifications

• Ph.D. in Applied Mathematics, Computational Science, Mechanical Engineering, Aerospace Engineering, Engineering Mechanics, 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.
• Due to the senstitivity of the work performed by the institute, position is restricted to US Citizens.
• Demonstrated experience in Bayesian inference, parameter estimation, uncertainty quantification.
• Demonstrated experience with computational modeling of physical or engineering systems.
• Experience with scientific programming using Python, MATLAB, Julia, C/C++, or similar computational tools.
• Demonstrated record of scholarly research, including peer-reviewed publications.

Preferred Qualifications

• Experience building and training artificial intelligence and machine learning models.
• Experience with common machine learning software packages such as PyTorch, Hugging Face, Jax, etc.
• Experience with active learning or Bayesian optimization.
• Experience with surrogate modeling of physical systems.
• 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.

Pay Band

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Overtime Status

Exempt: Not eligible for overtime

Appointment Type

Restricted

Salary Information

$65,000

Hours per week

40 hrs per week

Review Date

11/4/2026

Additional Information

This position is located in the Blacksburg, VA location and given the research area, this position will be restricted to a U.S. Citizen.

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.

Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic information, ethnicity or national origin, political affiliation, race, religion, sexual orientation, or military status, or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees or applicants, or on any other basis protected by law.

If you are an individual with a disability and desire an accommodation, please contact Alex Tuuriat [e-mail] regular business hours at least 10 business days prior to the event.