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Associate Research Staff - Computational Solid Mechanics

Oak Ridge National Laboratory
life insurance, parental leave, 401(k), retirement plan, relocation assistance
United States, Tennessee, Knoxville
Apr 27, 2026

Requisition Id16330

Overview:

We are seeking a Research Associate who will support the Deposition Science and Technology (DSaT) Group in the Manufacturing Science Division (MSD) at Oak Ridge National Laboratory (ORNL). MSD resides in the Energy Science and Technology Directorate (ESTD). DSaT performs research and development on the processing of metallic material systems for extreme environment and high temperature applications. The Research Associate will contribute to the development of thermo-mechanical modeling tools to support process development for advanced manufacturing processes and of process-microstructure-property-performance relationships for high-temperature alloys and extreme environment structural materials. A strong background in mechanical behavior of materials is required. Demonstrated experience in the implementation of nonlinear constitutive models in commercial (e.g. Abaqus, ANSYS, etc.) and/or open-source finite element (FE) codes (e.g., MOOSE, DAMASK, etc.) is required. Experience with microstructural modeling (e.g. crystal plasticity) and computational homogenization are preferred. You will be expected to collaborate with research staff and industry partners in the development of multiscale modeling methods to support certification and qualification efforts for components produced by various advanced manufacturing processes.

As part of our research team, you will engage with researchers with various backgrounds (e.g., materials science, mechanical behavior of materials, heat transfer) and interact closely with industry partners. These engagements will play a vital role in ensuring success of programs and the adoption by project sponsors, and in developing your network across academia and industry. You will also be involved in writing and supporting the development of proposals while taking the lead in publishing high impact papers.

Major Duties/Responsibilities:

  • Participate in the development and implementation of nonlinear constitutive models into FE codes.
  • Participate in the development and implementation of crystal plasticity constitutive models into FE codes for additively manufactured alloys.
  • Participate in the development of multiscale modeling approaches to establish process-structure-property-performance relationships for structural materials.
  • Apply advanced manufacturing process simulation tools to understand and mitigate the development distortion and residual stress.
  • Collaborate within a multi-disciplinary research environment consisting of computational scientists, experimentalists, and engineers conducting basic and applied research in support of the Laboratory's missions.
  • Present and report research results at workshops and conferences and publish key findings in peer-reviewed journals in a timely manner.
  • Ensure compliance with environment, safety, health, and quality program requirements per ORNL's Standards-Based Management System (SBMS).
  • Maintain strong dedication to the implementation and perpetuation of institution values and ethics.
  • Support senior staff in the development and execution of projects that provide valued and timely deliverables to the various stakeholders.
  • 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 and engineering, mechanical engineering, or related field.
  • Experience in the development and implementation of constitutive models within commercial and/or open-source finite element software.
  • A background in mechanical behavior of materials.

Preferred Qualifications:

  • Demonstrated expertise in multi-physics FE simulations is preferred.
  • Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred.
  • Excellent record of productive and creative research as demonstrated by publications in peer-reviewed journals.
  • Excellent written and oral communication skills.
  • Motivated self-starter with the ability to work independently, and to participate creatively in collaborative multi-disciplinary teams across the laboratory.
  • Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to changing needs/objectives.

Security, Credentialing, and Eligibility Requirements:

For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required. Additionally, ORNL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as mandated by Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation.

To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation. This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year. This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.

For foreign national candidates:

If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination to maintain employment. Once you meet the three-year residency requirement, you will be required to obtain a PIV credential to maintain employment.

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.

This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.

ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.

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