Argonne National Laboratory · 1 week ago
Postdoctoral Appointee - Scientific Machine Learning for Surrogate Modeling and Power Grid Dynamics
Lemont, IL
Full-time
Onsite
New Grad, Entry Level
$71K/yr - $118K/yr
0+ years expArgonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning. The role focuses on developing machine learning-based surrogates and emulators for the dynamics of power grids, ensuring trustworthy computations and scalability for enhanced efficiency and reliability.
EnergySecuritySocial Impact
No H1B
U.S. Citizen Only
Responsibilities
Conduct cutting-edge research in scientific machine learning, focusing on developing 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 the operational constraints of real-world power systems
Qualification
Required
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
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
Argonne National Laboratory
Argonne National Laboratory conducts researches in basic science, energy resources, and environmental management.
1001-5000 employees
Funding
Current Stage
Late StageTotal Funding
$41.4MKey Investors
Advanced Research Projects Agency for HealthUS Department of EnergyU.S. Department of Homeland Security
2024-11-14Grant· $21.7M
2023-09-27Grant
2023-01-17Grant
Leadership Team
Paul Kearns
Laboratory Director
Venkat Srinivasan
Director, Argonne Center for Collaborative Energy Storage Science (ACCESS)
Recent News
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