-
generating fusion energy. This research will focus on the chemical speciation and transport of tritium in the molten salt blankets using ab initio quantum simulations, machine learning potentials, and
-
workflows. The successful candidate will develop novel AI methods that integrate scientific knowledge, simulation, experimental data, and large-scale computing to achieve measurable AI advantage in scientific
-
environments, including operation of corrosion testing systems involving steam, sCO2, simulated exhaust gases, hydrogen, and other extreme conditions. Assist with sample preparation, microscopy workflows, and
-
theory, particularly electrodynamics. Experience working with an accelerator, such as in a control room, conducting accelerator-based experiments, or performing accelerator simulations. Experience
-
sponsors, isotope user communities, or other federal sponsors is preferred. Experience developing and implementing automation, process controls, modeling and simulation, process and equipment configuration
-
Requisition Id 16715 Overview: We are seeking a Postdoctoral Research Associate who will use multiscale modeling and simulation to develop probabilistic lifing frameworks for high-temperature alloys
-
, nuclear material tracking systems, and modeling and simulation tools that inform operational decision-making and policy. The Division collaborates extensively across ORNL and the broader DOE complex
-
manufacturing datasets, including sensor streams, in-process signals, post-process characterization data, simulation outputs, and digital twin data. Develop, integrate, and evaluate AI/ML models for anomaly
-
manufacturing datasets, including sensor streams, in-process signals, post-process characterization data, simulation outputs, and digital twin data. Design and implement scalable data engineering pipelines