13 phd-machine-learning research jobs at Argonne

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 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 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 -- 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 - Intelligent Learning for Light Source and Neutron Source User Measurements Including Navigation and Experiment Steering

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 Appointee – Connected and Automated Driving

the VMS team. Position Requirements PhD Degree in Mechanical/Electrical/Computer Engineering or Computer Science. Experience in development and experimentation of automated driving controls for cars and

Postdoctoral Associate in Numerical Optimization for Design of Experiments

soon-to-be completed PhD (typically within the last 0-3 years) in mathematics or a related discipline (e.g. computer science, engineering). Experience in mathematical optimization, machine/deep learning

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 Computer Vision for Materials Imaging

application. In your cover letter, please describe your previous experience with materials microscopy simulation/analysis; describe your experience with machine learning/computer vision; describe your software

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