Research Associate in AI/ML

Requisition Id 11569 

Overview

The National Security Sciences Directorate at Oak Ridge National Laboratory leads scientific and technological breakthroughs to confront some of the nation’s most difficult security challenges. We develop interdisciplinary applications needed for the security of our nation today and target our vision on how these challenges may manifest themselves in a decade or more. Our research and development focus on cybersecurity and cyber physical resiliency, data analytics, geospatial science and technology, nuclear nonproliferation, and high-performance computing for sensitive national security missions. We also enhance ORNL contributions to national security challenges by working closely with leading researchers at the lab in areas such as nuclear and chemical sciences and engineering, applied materials, advanced manufacturing, biosecurity, transportation, and computing.

We are currently seeking a qualified AI/ML scientist with specialty in AI robustness, distributed optimization, federated learning, uncertainty quantification to join our GeoAI group within the Geographic Data Science Section of Geospatial Science and Human Security Division. In this role, you will contribute to research and provide deep theoretical and applied expertise to design robust AI systems across emerging diverse geospatial national security applications. The position requires strong skills in applied mathematics, control and optimization, and statistical machine learning methods. The position affords the unique opportunity to work with a talented interdisciplinary team of R&D professionals, build new programs to advance the trust, safety and robustness of AI methods toward true impact on critical national security missions. The Geographic Data Science Section develops sensor technologies, analytical methods and models that collect, integrate, analyze and derive value from spatiotemporal data.

Within the GeoAI group, you will develop novel optimization, statistical learning and mathematical capabilities to advance the robustness, theoretical understanding of deep learning optimization, algorithmic understanding of complex dynamic systems, uncertainty quantification for space-time analytics and multi-modal geospatial data fusion methods.

Major Duties and Responsibilities:

• Lead research projects to formulate and develop robust AI systems, uncertainty quantification methods, develop federated learning systems, control and optimization methods, and statistical machine learning techniques for scientific knowledge discovery, integration and synthesis of information signals across heterogenous geospatial datasets.
• Design AI systems leveraging multiple foundation models in conjunction with traditional scientific modeling simulations and earth observation datasets.
• Conduct research on state-of-the-art learning techniques to expand the areas of federated learning, distributed optimization, explainability and trustworthiness, robustness and interpretability of AI systems on national security challenges.
• Forge collaborations with other researchers, both internally at ORNL and externally, on topics of mutual interest.
• Participate in the development of proposals for research projects.
• Publish research results in journal articles, conference papers, and technical manuals.
• Lead by example through exercising scientific integrity in proposing, performing, and communicating research.
• Lead by example to ensure all work is carried out safely, securely, and in compliance with ORNL policies, standards, and procedures.
• Exemplify a commitment to excellence in research, operations, and community engagement, and work cooperatively to leverage scientific capabilities across ORNL.
• Advance the reputation of the group members and standing of the group as a whole through establishing collaborations, professional society leadership and involvement, and organization of technical events at major conferences.
• Work in a highly collaborative environment with data scientists, machine learning scientists, remote sensing scientists, HPC engineers, physicists and geographers to deliver systems prototypes. 

Basic Requirements:

• Requires a Ph.D. graduate, a M.S. with a minimum of 4 years experiences, B.S. with 6 years of experience and a focus on electrical and computer engineering, applied mathematics, computer science or related fields (e.g. physics, statistics, climate science, etc.)
• Proven experience programming in Python using libraries like PyTorch, TensorFlow, Polars, and ONNX.
• Experience working on control and optimization, uncertainty quantification, robustness and interpretability of AI methods.
• Demonstrated experience collaborating on development of application codes and/or libraries for high performance computing resources.
• Excellent interpersonal skills with a demonstrated leadership ability and a strong commitment to a teaming environment. 
• Strong written and oral communication skills. 
• The ability to work in a dynamic, team environment. 

Preferred Qualifications:

• Experience working with spatio-temporal datasets, remote sensing imagery, text data, and time series analysis.
• Experience working on adversarial machine learning, federated learning, robustness and interpretability of AI, and transparency of machine learning.
• Hands-on experience with training machine learning models on high performance computing infrastructures leveraging GPU accelerators.
• Publication in top tier conferences
• Strong drive to learn new topics and skills, to develop innovative products for our customers, and build new programs to expand our capability.

Special Requirement:

This position is a Workplace Substance Abuse (WSAP) testing designated position. WSAP positions require passing a pre-placement drug test and participation in an ongoing random drug testing program.

These position reports to a Group Leader and work closely with R&D Section Heads to implement the group’s scientific vision; develop group members to enable their career advancement; establish capabilities that enable programs to excel at the forefront of science and technology; perform R&D to advance the field of knowledge and/or technology in one’s respective specialty; sets, implements, and models standards for performance of work consistent with Environment, Safety, Security, Health, and Quality (ESH&Q) requirements and business rules; and ensures a diverse and inclusive work environment where every employee feels safe, heard, and appreciated—a workplace that sets an example for the broader community.

The National Security Sciences Directorate conducts research and development to confront some of the nation’s most difficult security challenges and adversaries.  Our directorate houses S&T leadership in cybersecurity and cyber-physical resiliency, data analytics, geospatial science and technology, nuclear nonproliferation, and high-performance computing for sensitive national security missions. We draw on the Laboratory’s exceptional facilities and work closely with leading researchers in other areas at the lab such as nuclear and chemical sciences and engineering, applied materials, advanced manufacturing, biosecurity, transportation, and computing.  Our multi-disciplinary research teams are passionate about discovery and innovation as we create science-based solutions to complex security threats that put public safety, national defense, energy infrastructure, and the economy at risk.

Our commitment to diversity:  As we strive to become the world’s premier research institution in the sciences and technologies that underpin critical national security missions, we are committed to creating an inclusive environment that highly values a diverse workforce.  We recognize that a breadth of perspectives, insights, and experiences are necessary to drive the level of innovation and discovery that is mission critical to national security sciences.  Our commitment extends beyond our workforce to the next generation of researchers with STEM education outreach that seeks to engage a diverse range of students.

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.