OVERVIEW:

We are seeking a Postdoctoral Research Associate who will focus on creating innovative uncertainty quantification and visualization algorithms that enable trusted visual representation and analysis of large-scale 2D/3D scientific data. This position resides in the Data Visualization Group in the Data and AI Systems Section, Computer Science and Mathematics Division, Computing and Computational Sciences Directorate, at Oak Ridge National Laboratory (ORNL). This position presents a unique opportunity to develop cutting-edge high-performance computing (HPC) and machine learning/artificial intelligence (ML/AI) techniques that incorporate uncertainty into visualizations, enhancing the efficiency and reliability of scientific discovery. It also offers exciting prospects for direct collaboration with domain experts—such as those in neutron scattering and urban science—to apply and evaluate research methods on large, domain-specific scientific datasets.

BASIC QUALIFICATIONS:

• A PhD degree in Computer Science or a related discipline.
• A strong background in scientific data visualization, uncertainty quantification, AI/ML, statistics or a related field.
• Proficiency in Python and C++ programming languages.
• Excellent written and oral communication skills.

PREFERRED QUALIFICATIONS:

• Strong publication record with publications in journals/conferences related to scientific visualization, scientific computing, and/or uncertainty quantification.
• Strong experience in using techniques or tools relevant to scientific visualization, ML/AI, HPC, and statistics.
• 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.

SPECIAL REQUIREMENTS:

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 up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding.
Please submit three letters of reference when applying to this position. You may 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

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.

• Designing and developing foundational statistical/AI/data-driven techniques for uncertainty quantification and visualization of complex, large-scale 2D/3D scientific data.
• Publishing research in leading peer-reviewed journals and conferences.
• Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources.
• Collaboration with domain scientists for demonstration and validation of results.
• 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.