-
, machine learning, and control in the energy sector. The postdoc researcher will perform theoretical study and algorithm development on optimization/control/data analytics methods and authorize peer-reviewed
-
. The project will involve development of novel parallel algorithms to facilitate in-situ analyses at-scale for multi-million and multi-billion atom simulations. In this role, you can expect to work on enhancing
-
terms of universal computational skills. We encourage you to describe your work using broader technical terms—such as "statistical data analysis," "workflow automation," or "algorithm optimization"—rather
-
Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Knowledge in modeling and algorithms for large-scale ordinary differential equations (ODEs) and differential-algebraic equations (DAEs) Proficiency in a scientific programming language (e.g., C, C++, Fortran
-
methodologies and tools for economic and ecological analyses of hydropower systems. The position will involve the development and use of computer models, simulations, algorithms, databases, economic models, and
-
artifacts, and developing an independent research agenda in AI for science. Core responsibilities include: Leading research on foundation models, including problem formulation, algorithmic development, and
-
This position focuses on the research and development of novel radiation detectors and associated edge-computing circuits and algorithms for X-ray, particle, and nuclear physics experiments
-
electron beams, advanced beam-manipulation for precise electron-beam shaping, and ML for accelerator science. Responsibilities Develop and deploy ML algorithms for autonomous operations and optimization
-
technologies, control algorithms and powertrain architectures with focus on advanced technologies. The candidate will assist on projects to benchmark next generation vehicle systems, identify opportunities
-
beyond traditional error estimators, creating physics-based adaptation algorithms that intelligently predict where refinement will be most beneficial for smarter, more efficient simulations. We seek