UT-ORII Fellow: Automated Quality Control and Quality Assurance

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https://jobs.ornl.gov/job-invite/14704/



Overview: 

Oak Ridge National Laboratory (ORNL) is the largest US Department of Energy (DOE) science and energy laboratory, conducting basic and applied research to deliver transformative solutions to compelling problems in energy and security. Within ORNL, the Building Envelope Materials Research Group develops and deploys affordable, energy efficient and resilient building envelopes for new construction and retrofits to enable DOE's goals. Our synergistic research areas include building science, material and system development, and industrialized construction. With the support from the U.S. DOE Building Technologies Office, we have the most extensive building envelope research portfolio in the nation.


The Envelopes Group, in partnership with the University of Tennessee-Oak Ridge Innovation Institute (UT-ORII), is seeking an early career engineer or computer scientist to contribute to groundbreaking research on automated quality control and quality assurance that increase productivity, reduce errors, and lower cost in building construction and manufacturing. As an ORNL staff member, you will be part of a team of ORNL researchers and university research faculty in a UT-ORII Convergent Research Initiative focused on the development of state-of-the-art technologies for affordable manufacturing of energy-efficient, resilient buildings. In addition to being an ORNL staff member, since this role will be aligned with a UT-ORII convergent research initiative, you will also be considered a UT-ORII Fellow.



As an ORNL staff member and UT-ORII fellow, your career will develop in collaboration with researchers from both UT and ORNL through this early-career position at ORNL and with a Joint-Research Faculty (JFO) appointment at UT. As an integral part of the team, you will engage in a dynamic blend of activities. In addition to helping shape research programs, mentorship will be a key aspect of your role, guiding and inspiring graduate and undergraduate students. As a valued, early career researcher in both the ORNL Buildings and Transportation Science Division and UT-ORII, you'll have access to a rich network of resources, including seminars, training opportunities, and collaborations that will propel your career forward.



More About UT-ORII:

The University of Tennessee-Oak Ridge Innovation Institute is a partnership of Oak Ridge National Laboratory (ORNL) and UT created to prepare interdisciplinary leaders in energy, science, and technology and develop a world-class workforce for industry, government, and academia that will drive innovation and create the industries of the future. More information is available at University of Tennessee - Oak Ridge Innovation Institute (utorii.com). Today's energy economy is driven by disruptive technologies and swift change. The US is in a global competition for jobs, talent, and investment. To successfully compete, we must develop leadership talent in research and development (R&D), encourage entrepreneurship, and create an educational environment that promotes rapid innovation and attracts skilled professionals. Leveraging a 75-plus-year UT-ORNL partnership, UT and ORNL have developed joint institutes, joint facilities, interdisciplinary PhD programs, and comprehensive joint faculty arrangements, including 17 Governor's Chairs recruited for the significance of their impacts in their fields. 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 research on technologies that make new building construction and retrofits of existing buildings affordable, energy efficient, and resilient.

Design and develop digital tools to automate quality control and quality assurance processes in offsite and onsite building construction following standards and regulations.

Design and develop experimental setups, conduct experiments, and analyze results.

Perform simulations and laboratory and field experiments to develop proof of concepts.

Collaborate in multidisciplinary teams that are composed of ORNL researchers with various backgrounds (e.g., material science, non-destructive diagnostic tools, automation, advanced software, and sensors and controls among others).

Interact and collaborate with researchers from universities, national laboratories, and private industry.

Present research results to ORNL staff, DOE, industry, and academia in the form of invited talks, conference papers, and peer-reviewed journal papers with high impact factors.

Contribute to the development of ideas and the assembly of proposals.

Ensure compliance with environmental, safety, health, and program requirements.

Maintain a strong commitment to the implementation and continuation of ORNL's values and ethics.

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:

PhD in mechanical engineering, civil engineering, computer science or related discipline; or MS in mechanical engineering, civil engineering, computer science or related discipline and 2 to 7 years of relevant work experience.

Academic and research background in building construction along with computer vision, automation, and/or advanced sensing development and applications.

Knowledge of building construction.

Experience setting and conducting laboratory experiments.

Excellent oral and written communication skills.

Strong interpersonal skills to support team building.

Ability to work and demonstrate critical thinking individually and as part of a diverse team.

Ability to develop and/or maintain strong relationships through active participation in professional societies.

Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs.

Strong, proven publication record.

Self-motivated.



Preferred Qualifications:

At least 2 years of relevant work experience.

Proficiency with C++, Python, AI, machine learning.

Experience designing and assembling hardware and software systems for computer vision applications.

Experience implementing or developing image segmentation algorithms in complex environments.

Experience implementing or developing point cloud segmentation and analysis algorithms.



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.



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