55 mathematical-analysis-math-physics Postdoctoral positions at Oak Ridge National Laboratory
Sort by
Refine Your Search
-
physical characterization techniques (differential scanning calorimetry, dynamic light scattering, small angle neutron and/or x-ray scattering) to characterize the DIBs; and (3) Develop/implement image
-
materials. In this role, you will develop and apply methods that integrate physics‑guided image correction with intelligent (AI/ML‑enabled) data‑acquisition strategies. Key objectives include (1) implementing
-
challenges facing the nation. The Computational Coupled Physics (CCP) Group within the Computational Sciences and Engineering Division (CSED), at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral
-
Characterization Section of the Center for Nanophase Materials Sciences (CNMS), Physical Sciences Directorate (PSD) at Oak Ridge National Laboratory (ORNL). As part of our research team, you will investigate
-
in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
-
Proficiency in the use of industry standard modeling and simulation tools, such as spreadsheet-based process cost modeling, input/output modeling, and commercially available life cycle analysis tools such as
-
‑guided optimizations across languages (Julia/JACC, Mojo/MLIR, Rust/LLVM). Incorporate Enzyme-based automatic differentiation and multi-language IR tooling for AI‑driven analysis. High‑Productivity
-
Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE4 [#27230] Position Title: Position Location: Oak Ridge, Tennessee 37831
-
the Quantum Heterostructures Group in the Foundational & Quantum Materials Science Section, Materials Science and Technology Division, Physical Sciences Directorate at Oak Ridge National Laboratory (ORNL). As
-
Postdoctoral Research Associate in the areas Artificial Intelligence (AI) for Integrated Hydrology Modeling. The successful candidate will have a strong background in computational science, data analysis, and