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Virginia Tech

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Postdoctoral Associate, Intelligent Systems Division Virginia Tech in United States

Degree Level

Postdoc

Field of study

Computer Science

Funding

NIH-funded multi-institution research project; salary information listed as $65,000 per year.

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Country

United States

University

Virginia Tech

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Keywords

Computer Science
Biomedical Engineering
Mechanical Engineering
Biology
Aerospace Engineering
Mathematics
Artificial Intelligence
Women's Health
Uncertainty Analysis
Medical Science
Active Learning
Bayesian Statistics
Surrogate Modeling
Computational Modelling
Statistics
Programming Language
ML

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About this position

The Virginia Tech National Security Institute is seeking a postdoctoral associate to join a multidisciplinary research effort in computational modeling and uncertainty quantification. The position centers on building surrogate models for computational systems using reduced-order modeling and machine learning, then applying Bayesian and related methods to estimate parameters, quantify uncertainty, and suggest new training or full-order evaluation points to improve model performance.

The research is embedded in a multi-institution NIH-funded project focused on women’s health. The project aims to better understand and predict health outcomes by studying how hormones influence the structure and function of vaginal tissue across the lifespan. This gives the role a strong applied scientific context, combining computational methods with a biologically and medically motivated problem.

The postdoctoral associate will work within Virginia Tech’s Intelligent Systems Division and the broader VTNSI environment, contributing to peer-reviewed publications, scientific presentations, and student mentoring. The role also includes opportunities for conference travel and collaboration with researchers in Mathematics and Mechanical Engineering at Virginia Tech, as well as collaborators at other institutions.

Funding details

NIH-funded multi-institution research project; salary information listed as $65,000 per year.

What's required

Ph.D. in Applied Mathematics, Computational Science, Mechanical Engineering, Aerospace Engineering, Engineering Mechanics, or a closely related field is required; the PhD must have been awarded no more than four years before the effective appointment date with at least one year of eligibility remaining. Applicants must be U.S. citizens because of the sensitivity of the work. Required experience includes Bayesian inference, parameter estimation, uncertainty quantification, computational modeling of physical or engineering systems, scientific programming in Python, MATLAB, Julia, C/C++, or similar tools, and a record of scholarly research including peer-reviewed publications. Preferred qualifications include experience building and training AI/ML models, using PyTorch, Hugging Face, Jax, active learning or Bayesian optimization, surrogate modeling, independent research ownership, and mentoring students.

How to apply

Apply through the Virginia Tech careers portal using the posted application link. Prepare to meet the U.S. citizenship restriction and the required background for the research area. A criminal conviction check will be required if selected.

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