87 data-"https:" "https:" "https:" "https:" "UCL" "UCL" Postdoctoral positions at Argonne
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of funds. 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
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technologies, and in advancing data-driven risk monitoring approaches for supply chain resilience. The candidate will assist with data collection, analysis, and scenario modeling for a DOE-sponsored assessment
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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
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Leadership Computing Facility (ALCF), the Mathematics and Computer Science Division (MCS), the Computational Science Division (CPS), and the Data Science and Learning Division (DSL). The postdoctoral
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techniques to solve pressing challenges in energy storage. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne
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training or analysis of scaling behavior. Familiarity with challenges such as data heterogeneity, communication efficiency, or system constraints. Exposure to privacy, robustness, or security techniques (e.g
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will receive full consideration. Key Responsibilities AI-ready data and analysis for the ePIC Barrel Imaging Calorimeter and our Jefferson Lab program Support for the PRad-II and X17 experiments
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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
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Type Full time The expected 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
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Postdoctoral Appointee - Investigation of Electrocatalytic Interfaces with Advanced X-ray Microscopy
to the ISAAC data repository by generating AI-ready physical descriptors and advancing data-driven understanding of dynamic catalytic processes. Responsibilities include : Identifying relevant user systems and