Abdollah Shafieezadeh
3 months ago
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PhD positions in uncertainty quantification and uncertainty-aware machine learning at The Ohio State University The Ohio State University in United States
Degree Level
PhD
Field of study
Computer Science
Funding
The post announces PhD openings but does not specify stipend, tuition coverage, or other funding details.
Country
United States
University
Ohio State University
Suggested positions
Keywords
About this position
The RAMSIS Lab at The Ohio State University is recruiting two PhD students to work on uncertainty quantification (UQ), uncertainty-aware machine learning, computational modeling, and the risk and resilience of complex engineering and natural systems.
The lab is especially interested in students who want to develop new methodology rather than only apply existing tools. Research directions mentioned include stochastic modeling and uncertainty propagation, Bayesian inference and data assimilation, uncertainty-aware scientific ML and probabilistic learning, surrogate/reduced-order/multifidelity modeling, and risk-informed decision-making and optimization.
Experience connecting UQ and ML with physics-based computational models or large-scale engineering systems is particularly welcome. A strong engineering background and solid foundations in probability/statistics, scientific computing, or applied mathematics are highly valued.
The expected start is Fall 2027, with earlier starts in Spring or Summer 2027 also welcomed. Both domestic and international applicants are encouraged to apply.
To apply, email [email protected] with your CV and a brief introduction highlighting your relevant research experience or technical background, plus a few lines about the research problems that excite you. Use the subject line: RAMSIS PhD – UQ & Uncertainty-Aware ML – 2027.
Funding details
The post announces PhD openings but does not specify stipend, tuition coverage, or other funding details.
What's required
Applicants should have a strong engineering background and solid foundations in probability/statistics, scientific computing, or applied mathematics. Prior experience in stochastic modeling, uncertainty propagation, Bayesian inference, data assimilation, uncertainty-aware scientific machine learning, surrogate/reduced-order/multifidelity modeling, or risk-informed decision-making is relevant but not required in all areas. Candidates with experience connecting UQ and ML to physics-based computational models or large-scale engineering systems are especially welcome. Both domestic and international applicants may apply.
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Prepare your application
Review drafts and confirm requirements with the university.
Professors
Lichtenstein Professor
United States
Research Interests
Structural Engineering
Geotechnical Engineering
Earthquake Engineering
Infrastructure Resilience
Uncertainty Analysis
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.