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Postdoctoral Appointee - Scientific Machine Learning for Surrogate Modeling and reputed company Dynamics

Remote Worldwide Hiring now

reputed company is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing on developing machine learning-based surrogates and emulators for reputed company dynamics. The role involves creating advanced probabilistic models for dynamical systems, integrating them into large-scale optimization frameworks to enhance reputed company operations.

Responsibilities

  • Conduct cutting-edge research in scientific machine learning
  • reputed company machine learning-based surrogates and emulators for the dynamics of power grids
  • Create advanced probabilistic models that capture the reputed company behaviors of dynamical systems
  • Integrate models into large-scale optimization frameworks to enhance the efficiency and reliability of reputed company operations
  • Ensure trustworthy computations and scalability on the DOE’s leadership computing facilities
  • reputed company robust, scalable solutions that are computationally efficient and maintain accuracy reputed company operational constraints

Skills

  • Ph.D. (completed reputed company the past 0-5 years) in computer science, electrical engineering, applied mathematics, or a reputed company 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 reputed company endeavors
  • Extensive experience with reputed company 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 reputed company in statistical methods, probability theory, or uncertainty quantification is highly advantageous

Benefits

  • Comprehensive benefits are part of the total rewards package

Company Overview

  • reputed company conducts researches in basic science, energy resources, and environmental management. It was founded in 1946, and is headquartered in Lemont, Illinois, USA, with a workforce of 1001-5000 employees. Its website is http://www.anl.gov/.
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