Sort by
Refine Your Search
-
related to magnetic materials, experience with first-principles electronic structure methods and proven expertise in developing and/or applying advanced AI/ML methods for accelerated materials discovery
-
Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
Requisition Id 15685 Overview: The Center for Nanophase Materials Sciences (CNMS) is seeking a Postdoctoral Research Associate to support research directed towards developing novel AI/ML algorithms that can incorporate multi-scale computational simulations to aid with data fusion across multiple...
-
systems, including GPU-accelerated platforms. Develop and maintain scientific software, primarily in Python, using modern practices for software development, testing, documentation, and version control
-
accelerator-optimized methods — for large-scale numerical, scientific, and/or AI models. Demonstrating the scalability of new algorithms on leadership supercomputers for large-scale problems of national and
-
, optimize, and test advanced materials that will accelerate the deployment of higher performance nuclear energy systems. As part of our research team, you will assess the viability of accelerated irradiation
-
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
-
-performance computing, scientific modeling, and domain-informed AI to accelerate discovery across DOE mission areas such as energy, materials, biology, nuclear science, autonomous laboratories, and scientific
-
challenges and conduct the research needed to accelerate the delivery of solutions to the marketplace. The Reactor and Nuclear Fuel Cycle Analysis Group (RNFCA) Group is seeking candidates to apply
-
across the U.S. Gulf Coast. EGRET employs an integrated model–experiment (ModEx) approach accelerated by artificial intelligence (AI) to advance predictive understanding of how plant–microbial–soil
-
–experiment (ModEx) approach accelerated by artificial intelligence (AI) to advance predictive understanding of how plant–microbial–soil interactions vary across inundation and salinity gradients to shape