27 machine-learning research jobs at Argonne

Postdoctoral Appointee – Machine Learning for X-ray Science

position to develop and apply advanced analysis methods, including artificial intelligence and machine learning algorithms and approaches, for x-ray science and instruments. These methods will accelerate

Postdoctoral Research Associate - Machine Learning in High Energy Physics

The High Energy Physics Division (HEPD) at Argonne National Laboratory invites applications for a postdoctoral research associate position to conduct research in machine learning (ML

Postdoctoral Appointee – Machine Learning for Power Systems

in modeling, analysis and control of electric power distribution and transmission system, applying state of the art machine learning (ML) and deep learning algorithms to develop cybersecurity

Postdoctoral Appointee -- Physics-Informed Machine Learning/Hydrology

Argonne National Laboratory, a U.S. Department of Energy National Laboratory, has an opening for a Postdoctoral Appointee specialized in physics-informed machine learning at the Department

Postdoctoral Appointee - Hydrology/Machine Learning

for machine learning/AI model development development using Pytorch, Tesnor Flow or similar platforms. Utilization of high-performance computational platforms. Writing and modifying scientific code in Fortran/C

Postdoctoral Appointee - NST - Machine Learning for Electronic Structure and Materials Modeling

of machine learning in electronic structure calculations and advanced sampling. The postdoctoral researcher will work within the groups of Professor Giulia Galli (https://galligroup.uchicago.edu/ ) and Dr

Postdoctoral Appointee - NST - Experimental X-ray and Electron Characterization Data

and applying algorithms and software for the use of first principles and atomistic modeling, together with machine learning, to accelerate the inversion of experimental characterization data

Postdoctoral Appointee - X-ray Optics

techniques, and will also be instrumental in developing sophisticated data analysis software that incorporates machine learning and artificial intelligence. The successful candidate must be able to work onsite

Postdoctoral Appointee – CFD and ROM applications to Multiphase Flow Modeling

optimization with computer-aided design software. Knowledge of machine learning (using TensorFlow, PyTorch, etc.) for multi-fidelity modeling, regression tasks, management and analysis of large datasets, and

Postdoctoral Appointee – Mechanical Engineering

working with software packages or libraries using the finite element method. Experience working with the Multiphysics Object-Oriented Simulation Environment (MOOSE). Experience with PyTorch or other machine