-
Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate
-
key element of the two-beam acceleration concept Emphasize Bayesian optimization approaches and integrate these methods into the facility control system Design, execute, and analyze accelerator
-
statistical analysis and modeling techniques such as Gaussian process modeling, data assimilation, and Bayesian analysis; and 4. Open-source scientific software development. Expertise in computational
-
. Understanding of high-order methods for fluid flows. Understanding of turbulence, boundary layer flows, multi-phase flows, chemical kinetics, combustion, and detonations. Experience in mesh generation with
-
, including communication, networking, and leadership. Position Requirements To perform the essential functions of this position successful applicants must provide proof of U.S. citizenship, which is required
-
qubit-based quantum processors and connect them via a campus-scale fiber-optic network. The postdocs will design and fabricate superconducting transmon qubits and microwave-optical quantum transducers and
-
evaluate distribution networks in OpenDSS; investigate feeder- and system-level impacts of DERs (e.g., load flow, hosting capacity, voltage regulation). Develop and refine T&D co-simulation platforms (e.g
-
, networking, and leadership. Position Requirements Required Knowledge, Skills, and Experience: This level of knowledge is typically achieved through a formal education in economics, operations research, public
-
experts across system software, power management infrastructure, performance characterization, networking, and novel computer architectures and accelerators. It will also involve collaboration with leading
-
with physics-informed neural networks, automatic differentiation, neural ODEs, or other physics-aware DL techniques. Skill in programming languages such as Python, C/C++, Go, Rust etc. Ability to model