28 computer-vision-and-machine-learning Postdoctoral positions at Oak Ridge National Laboratory
Sort by
Refine Your Search
-
Postdoctoral Research Associate - Theory-in-the-loop of Autonomous Experiments for Materials-by-Desi
in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
-
physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
-
optical systems, thermal imaging, pyrometry, spectroscopy, high speed imaging or acoustic sensing. Familiarity with data analytics, machine learning, or signal processing. Knowledge of metal additive
-
, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The successful candidate will design and implement differential privacy
-
, dimensionality reduction, embeddings, etc.). Understanding of computational scaling techniques for machine learning and high-performance computing. Preferred Qualifications: Expertise in foundational models and
-
capabilities in a wide range of areas, including applied mathematics and computer science, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data
-
computing environments. Interest or experience in machine learning, inverse problems, or AI for scientific data. Strong record of research productivity and ability to work effectively in a collaborative
-
to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and
-
. Collaborate within a multi-disciplinary research environment consisting of computational scientists, computer scientists, experimentalists, engineers, and physicists conducting basic and applied AI/DL research
-
topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and iterative solvers. Successful applications will work