73 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL" positions at Argonne
Sort by
Refine Your Search
-
the ability and motivation to develop expertise in large-scale model training and scaling on HPC systems, as well as in handling the unique characteristics of scientific data, including large-scale numerical
-
spectroscopy (e.g., transient absorption), including laser operation, optical alignment, and data analysis Experience in synthetic inorganic chemistry and transition metal complex photophysics Job Family
-
(APS). The GL also contributes to the development of next-generation microscopy methods, instrumentation, and data workflows, while overseeing the group’s day-to-day operations, budget, user science
-
Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities
-
that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities
-
hiring range for this position is $72,879.00-$121,465.00. Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors
-
experimental electronics and timing systems (e.g., pulse generators, delay generators, gating electronics, and synchronization circuits). • Demonstrated proficiency in x-ray data analysis and instrument control
-
design, advanced modeling and high-performance computing, mathematics and data analytics, AI/ML algorithm development, and accelerator operations Ability to model Argonne’s core values of impact, safety
-
The Center for Nanoscale Materials (CNM) at Argonne National Laboratory seeks an outstanding postdoctoral researcher to advance data-driven, physics-informed AI for microelectronics materials
-
. The candidate is expected to lead an effort to prepare generalized ML techniques for data quality monitoring for tasks across multiple HEP experiments. Experiments with Argonne involvement include, but are not