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

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.

• 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.

Preferred Skills and Qualifications:

• 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.

Postdoctoral Appointee - Scientific Machine Learning for Surrogate Modeling and Power Grid Dynamics, Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department. All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.