Postdoctoral Research Associate - Structural Simulation and Machine Learning (ML) for Polymer Compos

Requisition Id 11986 

Overview: 

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an extraordinary 80-year history of solving the nation’s biggest problems. We have a dedicated and creative staff of over 6,000 people! Our vision for diversity, equity, inclusion, and accessibility (DEIA) is to cultivate an environment and practices that foster diversity in ideas and in the people across the organization, as well as to ensure ORNL is recognized as a workplace of choice. These elements are critical for enabling the execution of ORNL’s broader mission to accelerate scientific discoveries and their translation into energy, environment, and security solutions for the nation.

We are seeking a Postdoctoral Research Associate who will support the Composites Innovation Group in the Manufacturing Science Division (MSD), Energy Science and Technology (ESTD) at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate to support the composite manufacturing technologies through machine learning and physics-based simulations, specifically finite element analysis (FEA) for polymer composites. The candidate will also focus on developing a manufacturing process optimization framework for polymer composites through physics-based simulations, sensors from machines, and artificial intelligence.

Our research division carries out high quality R&D focused on the strategic composite material needs of the aerospace, automotive, and marine/shipbuilding industries as well as trending areas of international composite research in material science, mechanical and chemical engineering, and automation. The successful candidate will conduct research in the area of polymer composite manufacturing, specifically on the process optimization for the advancement of current manufacturing technologies, the development of the optimization framework, and the implementation of new optimization technologies.

Major Duties/Responsibilities: 

• Develop Finite Element Method (FEM) models and Machine Learning (ML) codes for Multiphysics simulations with coupled thermal and electromagnetic modules.
• Develop in-house software for pre- and post-process of Multiphysics simulations with coupled thermal, mechanical, and electromagnetic modules for world-leading high-performance computing platforms.
• Perform large-scale multi-physics and multi-scale simulations involving heat transfer, mechanical deformation, and electromagnetics modeling for various composite manufacturing processes and complex industrial components
• Develop and support the integration of a simulation-assisted process monitoring and control system for thermoplastic welding.
• Model ultrafast radio frequency heating for thermoplastic welding.
• Utilize machine learning algorithms to develop process optimization framework for polymer composite manufacturing
• Utilize various types of sensors and analyze the data from manufacturing equipment
• Utilize advance constitutive models and simulation tools for polymer composite analysis
• Interface with other researchers and technical staff regarding simulation, production, and characterization of the materials
• Publish original peer reviewed research articles
• Write invention disclosures and reports
• Supervise students/interns
• Present and report research results and publish scientific results in peer-reviewed journals in a timely manner
• Ensure compliance with environment, safety, health, and quality program requirements
• Maintain strong dedication to the implementation and perpetuation of values and ethics
• Ensure compliance with ORNL operation excellence
• Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote diversity, equity, inclusion, and accessibility by fostering a respectful workplace – in how we treat one another, work together, and measure success.

Basic Qualifications:

• A PhD in Materials Science and Engineering, Mechanical Engineering, Polymer Science and Engineering, or a related field completed within the last 5 years
• Expertise in computational solid mechanics
• Experience in polymer composite manufacturing

Preferred Qualifications:

• Prior knowledge and experience in developing Finite Element Method (FEM) models and working knowledge of Machine Learning (ML) algorithms.
• Demonstrated experience in developing, applying, and extending Finite Element Method (FEM) models and Machine Learning (ML) algorithms for application to composite mechanics and manufacturing, automated fiber placement (AFP) processes.
• Background in FORTRAN, C, and/or C++ applied programming and knowledge of Python, Java, or other scripting languages.
• Experience using parallel Linux computing platforms, parallel job submission scripts, standard software repository tools, and parallel visualization software.
• Experience in topology optimization for structural design
• Experience in thermomechanical property characterization of polymers
• Experience in sensors and monitoring devices
• An 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 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 ever changing needs

Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be for up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and the availability of funding.

Please submit three letters of reference when applying to this position. You can upload these directly to your application or have them sent to postdocrecruitment@ornl.gov with the position title and number referenced in the subject line.

Instructions to upload documents to your candidate profile:

• Login to your account via jobs.ornl.gov
• View Profile
• Under the My Documents section, select Add a Document

Benefits at ORNL:  

ORNL offers competitive pay and benefits programs to attract and retain talented people. The laboratory offers many employee benefits, including medical and retirement plans and flexible work hours, to help you and your family live happy and healthy. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also provided for convenience.

Other benefits include: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Life Insurance, Pet Insurance, 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.

If you have difficulty using the online application system or need an accommodation to apply due to a disability, please email: ORNLRecruiting@ornl.gov.

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.

If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.

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.