Description
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 (utorii.com). 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.
Major Duties/Responsibilities:
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.
Basic Qualifications:
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
Preferred Qualifications:
Experience with multiscale modeling, molecular simulation, or process modeling in materials or manufacturing contexts.
Experience applying AI/ML or big-data approaches to materials development, process optimization, recycling, or separations.
Knowledge of biobased materials, polymers, chemical processing, or recycling systems.
Familiarity with high-performance computing (HPC) environments.
Experience collaborating with national laboratories, industry, or government agencies.
Strong communication skills and ability to work effectively in embedded, domain-focused research teams
About ORNL:
As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation.
ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to support the well-being of you and your family. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also available for added convenience.
Other benefits include the following: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension Plan, Life Insurance, Disability Benefits, Generous Vacation and Holidays, Parental Leave, Legal Insurance with Identity Theft Protection, Employee Assistance Plan, Flexible Spending Accounts, Health Savings Accounts, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts.
