Postdoctoral Appointee - Scientific Machine Learning for Surrogate Modeling and Power Grid Dynamics

Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing on developing machine learning-based surrogates and emulators for power grid dynamics. The role involves creating advanced probabilistic models for dynamical systems, integrating them into large-scale optimization frameworks to enhance power grid operations. Responsibilities Conduct cutting-edge research in scientific machine learning Develop machine learning-based surrogates and emulators for the dynamics of power grids Create advanced probabilistic models that capture the complex behaviors of dynamical systems Integrate models into large-scale optimization frameworks to enhance the efficiency and reliability of power grid operations Ensure trustworthy computations and scalability on the DOE’s leadership computing facilities Develop robust, scalable solutions that are computationally efficient and maintain accuracy within operational constraints Skills Ph.D. (completed within the past 0-5 years) in computer science, electrical engineering, applied mathematics, or a related field Strong proficiency in Python, with additional experience in C, C++, or similar languages Demonstrated expertise in machine learning, especially in the context of dynamical systems modeled by differential-algebraic equations Experience with high-performance computing and the ability to scale models using distributed computing environments Excellent oral and written communication skills for effective collaboration across multiple teams Commitment to embodying the core values of impact, safety, respect, and teamwork in all endeavors Extensive experience with power grid models and large-scale optimization problems Familiarity with developing machine learning surrogates and emulators for dynamical systems Proficiency in managing large datasets and training with GPU-enabled computing resources Expertise in numerical optimization and familiarity with ML frameworks such as PyTorch, Jax, or TensorFlow A strong foundation in statistical methods, probability theory, or uncertainty quantification is highly advantageous Benefits Comprehensive benefits are part of the total rewards package Company Overview Argonne National Laboratory conducts researches in basic science, energy resources, and environmental management. It was founded in 1946, and is headquartered in Lemont, Illinois, USA, with a workforce of 1001-5000 employees. Its website is