Sort by
Refine Your Search
-
the last 5 years. Preferred Qualifications: Proven RDD&D experience in urban-scale building energy modeling. Experience planning and designing solutions for a variety of community projects, including
-
for Computational Sciences. They will collaborate with leading computer and computational scientists at ORNL and external collaborators in the development and application of new computational techniques specifically
-
Landscape analysis and modeling, with a focus on training and application of predictive machine learning tools. This researcher will synthesize regional-scale remote sensing imagery and ecological monitoring
-
passionate about leveraging their experience to enable real-world, quantifiable impacts across a variety of U.S. industries including chemical production, forest products, and critical minerals. The candidate
-
the development and optimization of products for a variety of industries from automotive to aerospace made from new bio- and waste-derived plastic resins and fillers. The ideal candidate for this role would be
-
delivering solutions to pressing energy storage problems essential to economic develop and security of the United States. As part of our research team, the candidate will be expected to work across a variety
-
–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
-
datasets. 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
-
datasets. 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
-
thermal systems, controls, or AI/ML applications Experience developing and deploying reinforcement learning algorithms for real-time control applications Experience with hardware-in-the-loop (HIL) testing