Sort by
Refine Your Search
-
development, and publication in peer-reviewed venues. Strong background in machine learning, with research experience in deep learning, foundation models, or related areas. Solid programming ability in Python
-
computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
-
The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
-
The High Energy Physics Division at Argonne National Laboratory invites applications for a postdoctoral research associate position to conduct research in machine learning (ML) for applications in
-
for AI and deep learning (details: NVIDIA DGX-2) Intel-based Aurora Supercomputer: A next-generation supercomputing system (details: Aurora Supercomputer) Additional advanced compute architectures designed
-
Infrastructure Sciences Division. Machine learning (ML), specifically deep learning (DL), has been demonstrated to successfully predict the weather for 1-14 days with skill on par with numerical weather prediction
-
novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
-
deep learning including data collection, architecture development, model training, and validation Interest in software development, with particular emphasis on the Python programming language and
-
values of impact, safety, respect, integrity, and teamwork Preferred Qualifications Deep understanding of AI/ML concepts, including transformers, latent-space representations, generative models, and
-
Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing