Research Scientist Materials and Manufacturing Modeling

Overview

Oak Ridge National Laboratory (ORNL) is seeking a Research Professional in simulation area to directly contribute to materials development and manufacturing R&D, with a focus on biobased materials, polymer and chemical processing, materials formulation, critical mineral separations and process technologies. Simulation, modeling, modern AI/ML and emerging technologies techniques will be used to support and deepen domain insight, guide experiments, and accelerate scale-up.

The role emphasizes tight coupling between materials science, chemistry, and manufacturing, supported by:

Multiscale modeling (material (molecular) → process → manufacturing (scale up))

Data-informed experimentation

Selective use of AI/ML and big-data techniques where they add real value

This position is partially supported by the University of Tennessee–Oak Ridge Innovation Institute (UT-ORII), and resides in the Composite Science and Technology Section in the Manufacturing Science Division (MSD) of the Energy Science and Technology Directorate at ORNL.

More About UT-ORII:

UT-ORII is a strategic partnership between ORNL and the University of Tennessee designed to develop interdisciplinary leaders in energy, science, and technology. Leveraging a 75+ year UT–ORNL collaboration, UT-ORII supports convergent research, joint institutes, interdisciplinary PhD programs, and leadership development that strengthen U.S. competitiveness in emerging industries. University of Tennessee - Oak Ridge Innovation Institute (). UT-ORII’s overall goal is to become a center for convergent research and talent development, helping maintain US prominence as a global innovation leader and providing tangible benefits to Tennessee.

• Conduct materials- and chemistry-centered research in sustainable manufacturing, including polymers, biobased and biological materials, chemical formulations, separations, and recycling systems.
• Apply simulation and modeling (e.g., molecular modeling, process modeling, multiscale approaches) to support materials development and manufacturing process understanding.
• Use AI, machine learning, and data-driven methods as enabling tools to accelerate experimentation, optimize processes, and extract insight from complex datasets.
• Design and execute experiments informed by modeling and simulation, ensuring tight integration between computation and physical systems.
• Advance manufacturing-relevant materials workflows, from formulation and processing to scale-up and deployment.
• Collaborate within multidisciplinary polymer and process teams spanning materials science, chemistry, chemical engineering, and mechanical engineering.
• Build strong collaborative links with ORNL’s digital manufacturing, AI, and HPC researchers (e.g., joint modeling or AI-enabled studies), while remaining embedded in applied polymer and process team.
• Work with industry partners to translate fundamental materials and manufacturing science into applied and scalable solutions.
• Lead and contribute to proposal development and execution for DOE and industry-sponsored research.
• 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.

• Ph.D. in materials science, chemistry, chemical engineering, polymer science, biological materials, or a closely related field.
• Demonstrated expertise in materials development, molecular-level chemistry, and/or manufacturing science.
• Experience with simulation or modeling relevant to materials or manufacturing systems.
• Working knowledge of data-driven methods, AI, or machine learning as applied tools (not necessarily as a primary research discipline).
• Strong programming or computational skills (e.g., Python, MATLAB, or similar) sufficient to support modeling and data analysis.
• Familiarity with manufacturing processes, materials processing, or scale-up considerations

• Experience with multiscale modeling, molecular simulation, or process modeling in materials or…