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 Requirements

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Required Skills and Qualifications:

• 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.

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