Sort by
Refine Your Search
-
Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
, or Julia) Experience in statistical modeling and probabilistic analysis Ability to model Argonne’s core values of impact, safety, respect, impact and teamwork Preferred skills, abilities, and knowledge
-
properties Expertise in chemical synthesis and analysis methods including NMR, FTIR, Raman spectroscopy, GC-MS, SEM/EDS, etc. Demonstrated scientific productivity through peer-reviewed publications, patents
-
structure-function analysis, and viral evolution Familiarity with deep mutational scanning datasets and methods for quantifying viral immune escape Strong publication record in relevant fields Ability to work
-
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
-
-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
-
Knowledge, Skills, and Experience: Proficiency in mathematical analysis and operator theory. Experience working with microelectronics. Experience in conducting synchrotron experiments and analyzing
-
modeling of crystals, dislocation dynamics, and defect analysis, linking atomic-scale simulations to macroscopic properties. Familiarity or interest in machine learning methods and computing frameworks
-
research projects, data analysis, physics interpretations, and reporting of the results. A strong background in the field of Experimental Nuclear Physics. Ability to model Argonne’s core values of impact
-
, the ALCF is studying the application of these techniques to a variety of our science applications, including but not limited to: Computational Chemistry, Plasma Physics, High Energy Physics, analysis