Postdoctoral Researcher - Bayesian ML & Uncertainty

Virginia Tech; Blacksburg (VA); On-site; USD 59,000 - 72,000; Full time

Job summary

Virginia Tech National Security Institute (VTNSI) seeks a Postdoctoral Associate to advance research in computational modeling and uncertainty quantification, developing surrogate models via reduced-order and machine-learning methods. The role includes Bayesian parameter estimation, uncertainty quantification, and guiding training data strategies for model improvement.

The position supports a NIH-funded project on women's health outcomes, collaborating with Mathematics and Mechanical Engineering

Qualifications

• PhD in a closely related field awarded within four years prior to appointment, with at least one year remaining.
• US Citizens only due to project sensitivity.
• Demonstrated experience in Bayesian inference, parameter estimation, and uncertainty quantification.

Responsibilities

• Develop surrogate models of computational systems using reduced-order or machine-learning approaches.
• Apply Bayesian methods to estimate parameters, quantify uncertainty, and identify new training or evaluation points.
• Prepare peer-reviewed publications and scientific presentations; mentor students involved in related research.

Skills

Bayesian inference

Parameter estimation

Uncertainty quantification

Computational modeling

Python

MATLAB

Julia

C/C++

AI/ML experience

Education

PhD in Applied Mathematics

PhD in Computational Science

PhD in Mechanical Engineering

PhD in Aerospace Engineering

PhD in Engineering Mechanics

Tools

Python

MATLAB

Julia

C/C++

Job description

Virginia Tech National Security Institute (VTNSI) seeks a Postdoctoral Associate to advance research in computational modeling and uncertainty quantification, developing surrogate models via reduced-order and machine-learning methods. The role includes Bayesian parameter estimation, uncertainty quantification, and guiding training data strategies for model improvement.

The position supports a NIH-funded project on women's health outcomes, collaborating with Mathematics and Mechanical Engineering