Postdoctoral Research Associate - Computational Solid Mechanics and AI​/ML

We are seeking a Postdoctoral Research Associate who will focus on theoretical and computational solid mechanics, including constitutive modeling, fracture and damage mechanics, and multiscale/multiphysics modeling of advanced materials and structural systems. This position resides in the Nuclear Infrastructure Deployment and Optimization (NIDO) Group, Nuclear Science and Engineering Division (NSED), Fusion and Fission Energy and Science Directorate (FFESD), at Oak Ridge National Laboratory (ORNL).

The position will focus on developing and applying advanced computational models for constitutive behavior, fracture, damage, degradation, and multiscale/multiphysics material behavior, with particular emphasis on heterogeneous and quasi-brittle materials such as concrete. Applications will include civil infrastructure, advanced construction technologies, nuclear structures, and other energy-related structural systems.

The research will also involve the integration of physics-based computational mechanics with artificial intelligence and machine learning (AI/ML), including physics-informed and hybrid modeling, surrogate modeling, and engineering data analytics.

Major Duties/Responsibilities:

• Conduct research in theoretical and computational solid mechanics, including constitutive modeling, fracture and damage mechanics, and nonlinear material behavior.
• Develop and apply mesoscale, multiscale, discrete, and finite element models for heterogeneous materials and structural systems.
• Develop coupled multiphysics models incorporating mechanical, thermal, transport, and environmental effects on material degradation and long-term performance.
• Implement and extend computational models using multiphysics finite-element frameworks such as MOOSE and discrete/mesoscale approaches such as the Lattice Discrete Particle Model (LDPM).
• Apply computational mechanics to evaluate structural performance, design margins, and the significance of construction deviations in civil and nuclear infrastructure.
• Develop and apply AI/ML methods for engineering applications, including physics-informed and hybrid modeling, surrogate models, and data analytics.
• Utilize experimental and characterization data to develop, calibrate, and validate comprehensive computational models.
• Perform large-scale simulations using high-performance computing platforms, including model verification, validation, and uncertainty assessment.
• Collaborate with computational researchers, experimental scientists, structural engineers, and other domain experts across ORNL.
• Publish research in peer-reviewed journals, prepare technical reports, and present results at conferences and technical meetings.
• Deliver ORNL's mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace in how we treat one another, work together, and measure success.

Basic Qualifications:

• A PhD in Civil Engineering, Mechanical Engineering, Engineering Mechanics, Materials Engineering, or a related discipline within the past five years.
• Strong background in solid mechanics, including constitutive modeling, fracture mechanics, damage mechanics, or nonlinear material behavior.
• Demonstrated experience in computational mechanics, including finite element, discrete, mesoscale, or multiscale modeling approaches.
• Experience developing or applying numerical models for material or structural behavior.
• Scientific programming experience using Python, C++, or similar languages.
• Experience with Linux-based computational environments and scientific software development tools.
• Excellent written and oral communication skills, with demonstrated ability to work independently and collaboratively in multidisciplinary research teams.

Preferred Qualifications

• Strong background in computational fracture and damage mechanics, constitutive modeling, and heterogeneous or quasi-brittle materials.
• Experience with LDPM, lattice/discrete models, mesoscale mechanics, or related computational approaches.
• Experience with multiphysics finite-element frameworks such as MOOSE and high-performance computing.
• Knowledge of coupled mechanical, thermal, transport, and environmental degradation processes.
• Experience with AI/ML for engineering applications, particularly physics-informed or hybrid modeling, surrogate models, and scientific data analytics.
• Demonstrated…