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Oak Ridge National Laboratory
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Postdoctoral Research Associate in Computational Solid Mechanics and AI/ML Oak Ridge National Laboratory in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
ORNL offers competitive pay and employee benefits, including medical, retirement, prescription drug, dental, vision, 401(k), pension, life insurance, disability benefits, leave, wellness and assistance programs, and possible relocation assistance. The appointment is up to 24 months, with potential extension subject to performance and funding availability. No specific salary or stipend amount is stated.
Country
United States
University
Oak Ridge National Laboratory
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About this position
Oak Ridge National Laboratory is seeking a Postdoctoral Research Associate in computational solid mechanics and artificial intelligence/machine learning. The position is based in the Nuclear Infrastructure Deployment and Optimization Group within ORNL’s Nuclear Science and Engineering Division and Fusion and Fission Energy and Science Directorate.
The research focuses on theoretical and computational solid mechanics, including constitutive modeling, fracture and damage mechanics, nonlinear material behavior, and multiscale and multiphysics modeling. The researcher will study heterogeneous and quasi-brittle materials such as concrete, with applications to civil infrastructure, advanced construction technologies, nuclear structures, and other energy-related structural systems.
Key work will include developing mesoscale, multiscale, discrete, and finite-element models; modeling mechanical, thermal, transport, and environmental degradation processes; and implementing computational approaches using frameworks such as MOOSE and the Lattice Discrete Particle Model. The project also addresses structural performance, design margins, construction deviations, long-term material behavior, and uncertainty assessment.
A major component of the role is integrating physics-based computational mechanics with AI/ML, including physics-informed and hybrid modeling, surrogate models, and engineering data analytics. The researcher will use experimental and characterization data to calibrate and validate models, conduct large-scale simulations on high-performance computing platforms, publish findings, prepare technical reports, and collaborate with computational researchers, experimental scientists, structural engineers, and other specialists across ORNL.
The research environment combines national-laboratory computational science, materials and structural engineering, experimental validation, multiphysics simulation, and energy-related infrastructure research.
Funding details
ORNL offers competitive pay and employee benefits, including medical, retirement, prescription drug, dental, vision, 401(k), pension, life insurance, disability benefits, leave, wellness and assistance programs, and possible relocation assistance. The appointment is up to 24 months, with potential extension subject to performance and funding availability. No specific salary or stipend amount is stated.
What's required
Applicants must have a PhD in Civil Engineering, Mechanical Engineering, Engineering Mechanics, Materials Engineering, or a related discipline awarded within the past five years, and must complete all degree requirements before starting. Required qualifications include a strong background in solid mechanics; experience with computational mechanics and numerical modeling; scientific programming in Python, C++, or similar languages; Linux-based computational environments and scientific software development tools; and excellent written and oral communication skills with the ability to work independently and collaboratively. Applicants must submit two reference letters. The position requires a pre-hire physical, the ability to obtain and maintain an HSPD-12 PIV badge or applicable LSSO determination, and successful federal access and background requirements.
How to apply
Apply through the ORNL careers portal using the “Apply now” link. Submit the required application documents and two reference letters, either through the candidate profile or by sending them to [email protected] with the position title and number in the subject line.
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