Sort by
Refine Your Search
-
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
-
(AI/ML), neuromorphic sensing, video super-resolution, electromagnetics, real-time systems, data mining and analysis, high performance computing, cyber security protection for radar and related training
-
for Computational Sciences (NCCS). The group creates and deploys workflow, data, and AI-agent technologies that connect leadership-class computing with experimental and observational science. Its primary goal is to
-
interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Basic Qualifications: A PhD in Earth and Environmental Sciences, Ecology, Biosciences, Botany, Geosciences, or a related
-
PhD is strongly preferred. Candidates must have demonstrated leadership experience in line management and organizational operations, including supervision of technical leaders, workforce development
-
time-of-flight secondary ion mass spectrometry (ToF-SIMS), scanning electron microscopy (SEM), and X-ray diffraction (XRD). Experience in data reduction of big spectroscopy, mass spectrometry, and image
-
significant data collection and interpretation, assessment of results, and incorporation of steps for continuous improvement of processes and communication of activities to senior research staff and management
-
significant data collection and interpretation, assessment of results, and incorporation of steps for continuous improvement of processes and communication of activities to senior research staff and management
-
Genesis Mission , candidates are encouraged to also articulate how their research will leverage or advance AI, advanced computing, data, automation, and other emerging capabilities to accelerate discovery
-
Application-driven Composable Distributed Storage. The candidate will be able to make research contributions in understanding and efficient use of distributed data storage and I/O subsystems for High