Sort by
Refine Your Search
-
specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors
-
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
-
for dynamics imaging. The primary goal of this project is to develop single-frame ptychography methods that eliminate the need for scanning, enabling fast imaging and the visualization of dynamic processes in
-
scattering, x-ray circular dichroism, photoelectron spectra, and nonlinear x-ray signals Model ultrafast molecular dynamics using time-resolved observables, including numerical solutions of the time-dependent
-
devices, nonlinear optics, or microwave photonics Working knowledge of simulation tools such as COMSOL or Lumerical for electro-optic modeling Desirable Skills Proficiency in Python-based data analysis
-
diamond membranes, defect synthesis, quantum experiment development, and optical spectroscopy. The successful candidate will join a dynamic, collaborative team working across the Argonne community and with
-
The Advanced Photon Source (APS) (https://www.aps.anl.gov/ ) at Argonne National Laboratory (Lemont, Illinois, US (near Chicago)) invites applicants for a postdoctoral position to develop and
-
(microelectromechanical systems) devices for X-ray optics at synchrotron radiation sources. Some background of the project is given in the publications listed below. The idea is to make highly nonlinear MEMS-based
-
). This position will focus on ultrafast dynamics in femto- to nanosecond time-domains in quantum materials including nonequilibrium phase transitions and collective excitations in quantum materials, including
-
the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis