Postdoctoral Appointee

**Job Description**
This Postdoctoral Appointee position at Argonne National Laboratory focuses on conducting cutting-edge research in scientific machine learning, specifically developing machine learning-based surrogates and emulators for power grid dynamics. The role involves creating advanced probabilistic models to capture complex behaviors of dynamical systems, integrating them into large-scale optimization frameworks to enhance power grid efficiency and reliability. The appointee will be responsible for the conceptual framework, design, and implementation of these models, ensuring trustworthy computations and scalability on DOE’s leadership computing facilities, with a focus on robust, scalable, computationally efficient solutions within real-world power system operational constraints.

**Skills & Abilities**
• 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 (Preferred).
• Familiarity with developing machine learning surrogates and emulators for dynamical systems (Preferred).
• Proficiency in managing large datasets and training with GPU-enabled computing resources (Preferred).
• Expertise in numerical optimization and familiarity with ML frameworks such as PyTorch, Jax, or TensorFlow (Preferred).
• A strong foundation in statistical methods, probability theory, or uncertainty quantification is highly advantageous (Preferred).

**Qualifications**
Required Degree(s) in:
• Computer Science
• Electrical Engineering
• Applied Mathematics
• Related field

**Experience**
Other:
• Ph.D. completed within the past 0-5 years

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