78 postdoc-computational-physics "Data driven Materials Modeling" Postdoctoral positions at Oak Ridge National Laboratory
Sort by
Refine Your Search
-
challenges facing the nation. We are seeking a Postdoctoral Research Associate who will support the Quantum Sensing and Computing Group in the Computational Science and Engineering Division (CSED), Computing
-
, patents, journal papers, and conference publications, participate in proposals, and estimate costs. Basic Qualifications: A PhD degree in physics, optical or electrical engineering, nuclear engineering, or
-
program Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a
-
Requisition Id 16217 Overview: The Multiscale Biomedical Systems Group within the Advanced Computing in Health (ACH) section of the Computational Sciences and Engineering Division (CSED) at Oak
-
another, work together, and measure success. Basic Qualifications: A PhD related to computational or theoretical condensed matter physics, theoretical chemistry, theoretical materials science, or other
-
for the Postdoctoral Research Associate, Advanced Nuclear Reactor and Fuel Cycle Engineer role. This role is responsible for working with state-of-the-art modeling and simulation capabilities for lattice physics
-
Requisition Id 16764 Overview: The National Center for Computational Sciences (NCCS) at the Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral research associate in the area of
-
traits, plant-microbe-soil interactions, physical and chemical soil properties, and organo-mineral associations. This candidate will directly support the Exploring Gulf Region Ecosystem Transitions (EGRET
-
Requisition Id 16798 Overview: The Data and AI Systems Research Section/Workflow systems Group within the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) is
-
that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and