Job Description
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 specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors of dynamical systems, which will be integrated into large-scale optimization frameworks to enhance the efficiency and reliability of power grid operations.
The Postdoctoral Appointee will be responsible for the conceptual framework, design, and implementation of these machine learning models, ensuring trustworthy computations and scalability on the DOE’s leadership computing facilities. The focus will be on developing robust, scalable solutions that are computationally efficient and maintain accuracy within the operational constraints of real-world power systems.
**Position Require...
The Postdoctoral Appointee will be responsible for the conceptual framework, design, and implementation of these machine learning models, ensuring trustworthy computations and scalability on the DOE’s leadership computing facilities. The focus will be on developing robust, scalable solutions that are computationally efficient and maintain accuracy within the operational constraints of real-world power systems.
**Position Require...
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