13 Statistics-"University-of-Leeds--------" Postdoctoral positions at Argonne in Ireland-United-Kingdom

Postdoctoral Appointee – Enhanced statistical methods in computational thermodynamics

This is an opportunity for a knowledgeable and creative individual to be part of a team developing statistical methods for model selection, parameter inference, and uncertainty quantification and

Postdoctoral Appointee – Mechanical Engineering

mechanics models and statistical reliability models (e.g. Weibull failure models). Materials of interest include high-temperature alloys, ceramics, and ceramic matrix composites. The goal is to enhance

Postdoctoral Appointee - Critical Material Market Modeling

. Contribute to ongoing development and analysis using GCMat, an existing agent-based model of critical material supply chains. Use advanced statistical analysis to estimate mine development costs and associated

Postdoctoral Appointee: Critical Materials and Manufacturing

, statistical analysis, and predictive modeling Familiarity with economics as it applies to mining, energy industries, and manufacturing Familiarity with resource extraction and processing practices Experience

Postdoctoral Appointee – Machine Learning for Power Systems

engineering package (e.g. pandas). Solid foundation in mathematics/statistics with experience in cyber-physical systems modeling and analysis. Ability to work both independently and collaboratively in a team

Postdoctoral Appointee – Energy Economist

, applied economics. Experience in statistics and statistical analysis. Experience with environmental systems is preferred but not required. Experience working with federal agencies is preferred but not

Postdoctoral Appointee Energy Systems Modeling and Life Cycle Analysis for Industrial Decarbonization

integrated assessment models. In several instances, engineering and/or statistical models would need to be developed to characterize input datasets for energy use, emissions, performance, and cost attributes

Postdoctoral Appointee -- Physics-Informed Machine Learning/Hydrology

) on HPC systems Skilled in data analysis, statistics, and visualization, especially on large datasets. Knowledge of developing flood observation training datasets from multiple sources. Experience in

Postdoctoral Researcher –Feedstock Production Life Cycle Analysis

literature and statistics from US Department of Agriculture and other agencies. The candidate will work on projects to evaluate the synergies between the decarbonization of U.S. agriculture and the production

Postdoctoral Appointee -- Large-Scale Hydrodynamic Modeling/Hydrology

analysis, statistics, and visualization, especially on large datasets. Experience in writing and modifying scientific code in Fortran, C, C++, CUDA and Python. Effective written and oral communication skills