**Job Description**
The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory seeks a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning. The primary focus is on developing machine learning-based surrogates and emulators for power grid dynamics. This role involves creating advanced probabilistic models to capture 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 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 objective is to develop robust, scalable, and computationally efficient solutions that maintain accuracy within the operational constraints of real-world power systems.
**Skills & Abilities**
• Strong proficiency in Python.
• 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.
• 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 (Preferred).
• Familiarity with ML frameworks such as PyTorch, Jax, or TensorFlow (Preferred).
• Strong foundation in statistical methods, probability theory, or uncertainty quantification (Highly advantageous).
**Qualifications**
Required Degree(s) in:
• Computer Science
• Electrical Engineering
• Applied Mathematics
• Related field
**Experience**
Experience Required:
• 0-5 years (Ph.D.)
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