Sort by
Refine Your Search
-
separation, and biomass based chemical and fuel precursors. Selected candidate will be responsible for developing new and exciting ideas, building collaboration within and outside ORNL, and executing
-
the computational foundations of probabilistic programming, such as automatic differentiation, tensor libraries (PyTensor, JAX), gradient-based samplers, or model transpilation and compilation. Experience with
-
platform. The successful candidate will (1) integrate heterogeneous sensors and onboard computing hardware; (2) develop methods for LiDAR-based simultaneous localization and mapping (SLAM), autonomous
-
; expanding fundamental uranium material science Perform uranium material synthesis using a variety of solution-based and/or solid-phase materials chemistry techniques Conduct analysis of nuclear fuel cycle
-
. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
-
Requisition Id 17107 Overview: We are seeking a postdoctoral researcher focused on the development of in situ interfacial characterization techniques for silicon-based materials and to map
-
candidate will manage several projects concurrently, help shape new research directions within their field, and collaborate closely with ORNL staff, industry professionals, and academic partners. Based within
-
-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
-
Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI workflows for grid
-
Requisition Id 16802 Overview: We are seeking a Postdoctoral Research Associate for the development and application of advanced multiphysics simulations, and machine learning (ML) methods relevant