Publisher
source

Pınar Acar

1 month ago

PhD Position in Uncertainty Quantification and Bayesian Inference for Computational Modeling Virginia Tech in United States

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Country flag

Country

United States

University

Virginia Tech

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Keywords

Computer Science
Mechanical Engineering
Mathematics
Python Programming
Uncertainty Analysis
Bayesian Statistics
Computational Modelling
Statistics
Programming Language

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About this position

ASTRO Lab at Virginia Tech is recruiting a PhD student for Spring 2027 in uncertainty quantification (UQ) and Bayesian inference for computational modeling.

The opening is posted by Pınar Acar, Associate Professor at Virginia Tech, who is seeking a student for her group. The research focus is highly quantitative and computational, with emphasis on UQ, Bayesian methods, and scientific programming.

Eligibility highlights: an MSc degree is strongly preferred; prior research experience in UQ and Bayesian methods is required; and strong programming skills in Python, MATLAB, or C/C++ are expected. The post explicitly asks only qualified candidates to reach out.

How to apply: email a CV and a short note describing your specific experience in UQ and Bayesian methods to [email protected]. The post does not mention a formal portal or deadline beyond the Spring 2027 start.

Funding details

Available

What's required

An MSc degree is strongly preferred. Prior research experience in uncertainty quantification and Bayesian methods is required. Strong scientific programming skills in Python, MATLAB, or C/C++ are required. Applicants should only reach out if they meet these criteria.

How to apply

Email Pınar Acar with a CV and a short note describing your specific experience in uncertainty quantification and Bayesian methods. Use [email protected].

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Professors

Associate Professor

University Name
Country flag

United States

Research Interests

Statistics

Mathematics

Mechanical Engineering

Bayesian Statistics

Python Programming