48 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"Cardiff-University" positions at Argonne
Sort by
Refine Your Search
-
independently conducting machine studies to diagnose and resolve operational issues. Support APS performance improvements by conducting accelerator experiments, processing and analyzing data, and performing
-
operations is preferred, working knowledge of machine learning and artificial intelligence methods is highly desirable The successful candidate will demonstrate expertise in accelerator physics, accelerator
-
on developing machine-learning surrogates for electronic structure and electrostatic potential and using these models to predict structural and electronic evolution under applied bias. Methods may include density
-
science, including electronic structure methods molecular dynamics, and scientific machine learning. Experience with High-Performance Computing (HPC) systems and intelligent workflows. Demonstrated
-
Computer Science, Artificial Intelligence, Machine Learning, Computational Biology, Bioinformatics, or a related field. Strong programming skills in Python, with experience in AI/ML frameworks (PyTorch, JAX, Hugging
-
computer-aided design software. Collaborative skills, including the ability to work well with other divisions, laboratories, and universities. Ability to demonstrate strong written and oral
-
, including both the large-scale production machines and the testbed machines featuring novel architectures such as Cerebras and SambaNova. The list below provides examples of the potential tasks
-
microelectronics project. To learn more: Argonne to lead two microelectronics research projects under U.S. Department of Energy initiative | Argonne National Laboratory Position Requirements Recent or soon-to-be
-
skills and familiarity with LLM APIs (e.g., OpenAI API), agent frameworks (e.g. LangChain), PyTorch, and the Python scientific stack (e.g., numpy, pandas, scikit-learn). Experience with front-end
-
. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be