Postdoctoral Research Associate - AI/HPC for Distributed Energy Resource Optimization

Requisition Id 13050 

Overview: 

We are seeking a Postdoctoral Research Associate who will support a growing portfolio of research in large language models, large vision models, model vulnerability assessment, privacy preserving federated learning techniques, and knowledge distillation to target resource-constrained training and inference, especially in edge computing scenarios at Oak Ridge National Laboratory (ORNL).  

 

As part of the Geo AI group, you will support and lead research tasks related to deploying AI advances toward distributed energy resource optimization needs. The GeoAI Group is under the Geospatial Science and Human Security Division (GSHSD) at ORNL. The group performs artificial intelligence, computer vision, and federated learning research initiatives, with emphasis on large scale geospatial data analysis. Under the mentorship of senior research staff, a selected applicant will take roles on multidisciplinary teams supporting ground breaking research and engineering with large-scale distributed geospatial workflows, using GPU-based high-performance computing (HPC) across multiple platforms. 

 

History: 

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 encourage 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. 

 

Major Duties and Responsibilities: 

• Develop workflows that integrate with existing or new LLMs and LVMs for resource optimization with energy grid data. 

• Provide coding support to implement privacy preserving federated learning techniques. 

• Support the design of knowledge distillation methods for resource-constrained training and inference for edge computing scenarios 

• Publish research results in journal articles, conference papers, and technical manuals. 

• Ensure all work is carried out safely, securely, and in compliance with ORNL policies, standards, and procedures. 

• Commit to excellence in research, operations, and community engagement, and work collaboratively to useR scientific capabilities across ORNL. 

• Collaborate with data scientists, machine learning scientists, remote sensing scientists, HPC engineers, Energy grid subject matter experts, and geographers to deliver prototypes. 

 

Basic Requirements: 

• Requires a Ph.D. in electrical and computer engineering, computer science, applied mathematics or related area, completed within the last 5 years 

• Experience in developing AI/ML methods for analyzing large-scale observation based or simulated datasets 

• Strong research profile and be able to conduct independent research 

• 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, simulations, and time series analysis 

• Experience in development and evaluation of energy grid data, natural language processing applications 

• Hands-on experience with training machine learning models on high performance computing infrastructures with GPU accelerators 

• Experience in the development of project research proposals 

• Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks 

• You'll report 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 Direc