Sort by
Refine Your Search
-
environment? If so, the Oak Ridge National Laboratory’s Learning Systems Group within the Data and Artificial Intelligence Systems section invites you to apply to our new postdoctoral research associate
-
scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation Experience applying machine learning or AI to molecular simulation
-
with world leaders in catalysis and electrochemistry through national laboratory consortia of the Department of Energy’s Office of Critical Minerals and Energy Innovation (CMEI). Your role will be
-
, National Security Sciences Directorate at Oak Ridge National Laboratory (ORNL). Major Duties/Responsibilities: Support with designing, configuring, and running advanced cybersecurity experimentation testbeds
-
, Materials Science, Engineering Mechanics, Manufacturing Engineering, Mechanical Engineering, Artificial Intelligence/Machine Learning, or a related field completed within the last 5 years Preferred
-
and machine-learning-driven optimization frameworks for polymer composite manufacturing processes. This position resides in the Composites Innovation Group in the Manufacturing Science Division (MSD
-
and validating scattering‑correction and calibration workflows that yield quantitative attenuation coefficients, and (2) designing adaptive tomography approaches that reduce acquisition time while
-
competing structural phases and the vibrational and electronic structure in materials with defects and disorder. This effort will further seek to implement strategies to leverage machine learning techniques
-
include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced
-
. This position focuses on researching, designing, and deploying innovative data pipelines and readiness frameworks to tackle obstacles such as data heterogeneity, scalability bottlenecks, privacy