12 architecture positions at Argonne in United States

Postdoctoral Appointee – Machine Learning for Power Systems

learning architectures. Familiarity with federated learning, and game theoretic modeling. Experience or familiarity with hardware-in-the-loop is helpful but not required. Application Procedure: Interested

Postdoctoral Appointee - Quantum Materials

be part of a dynamic team working collaboratively with researchers in both the Q-NEXT Materials and Device Integration and Quantum Communication thrusts on optimal hybrid device architectures

Postdoctoral Appointee – Low-Carbon Fuel Sprays and Engine Combustion Modeling

experimentalists, CFD experts, and computational scientists to enable cutting-edge CFD modeling & simulations on the next generation supercomputing architectures. Position Requirements Ph.D. in mechanical/aerospace

Postdoctoral Appointee - MSD Midwest Integrated Center for Computational Materials (MICCoM)

-center.org/software.html ). Excellent candidates are sought with experience in the development of complex scientific software architectures to support the development and deployment of MICCoM codes on pre- and

Postdoctoral Appointee -- Physics-Informed Machine Learning/Hydrology

networks, hybrid modeling, and regularization techniques. Experience with various neural network architectures (e.g., graph neural networks, autoencoders, generative adversarial networks, etc.) Understanding

Postdoctoral Appointee – Atomic Layer Deposition of 2D Materials

of energy-efficient microelectronics. The successful applicant will spearhead efforts in simulating, optimizing, and benchmarking devices and architectures based on 2-dimensional (2D) semiconductor materials

Postdoctoral Appointee - Experimental Quantum Communication and Networking (InterQnet)

architectural choices that can be applied to heterogeneous national-scale quantum networks.  The focus of this position is the development of a networking node based on solid-state erbium (Er3+) ions coupled

Research Scientist

involving engine modelers and computational scientists to run the simulations at-scale using both current large clusters and the next generation supercomputing architectures. Work to consistently meet

Postdoctoral Appointee – Gas Turbine Modeling

scientists to run the simulations using the next generation supercomputing architectures. Present and publish results in peer reviewed society technical reports, journal articles, and meetings with key

Postdoctoral Appointee – Machine Learning for Power System

ETL pipelines to ingest multi-modal time series data for ML model training. Develop ML models using CNN, LSTM, and Transformer architectures for applications like anomaly detection, time series