11 experiment-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"FEMTO-ST" Postdoctoral positions at Argonne
Sort by
Refine Your Search
-
The High Energy Physics Division at Argonne National Laboratory (ANL) invites applications for a Postdoctoral Research Associate position to join our team working on the ATLAS experiment at the Large
-
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
-
availability. Relevant Publications: 1. P. Chen et al., Ultrafast photonic micro-systems to manipulate hard X-rays at 300 picoseconds, Nat Commun, 10:1158 (2019). https://doi.org/10.1038/s41467-019-09077-1. 2
-
ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experiment, and who is motivated to translate computational advances into real
-
solver, capable of running simulations on the Aurora supercomputer, using AMReX ( https://amrex-codes.github.io/amrex/ ) and the lattice Boltzmann method (LBM). The candidate will develop flow/geometry
-
available. Explore agentic AI approaches for federated learning, including AI agents that can assist with task orchestration, experiment planning, model evaluation, workflow automation, and decision
-
research program at the interface of theory and experiment. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of physical chemistry, inorganic chemistry
-
responsibilities will be the development of AI models for robotic control and the demonstration of these methods via simulation and experiment. Beyond the listed projects, the candidate may contribute to
-
are involved in SpinQuest at Fermilab and the MUSE experiment at PSI. Our hardware program includes the ePIC Barrel Imaging Calorimeter, and instrumentation R&D such as a polarized lithium-ion source
-
datasets, spanning the full experimental cycle from real-time X-ray measurements to post-experiment reconstruction: Develop learning-enabled algorithms for 3D reconstruction of noisy and heterogeneous