Sort by
Refine Your Search
-
energy supply systems, multi-objective and stochastic optimization, advanced statistical analysis, and data visualization. This position offers the opportunity to work with a multidisciplinary team of
-
, or spectroscopy datasets. Familiarity with total scattering analysis, pair distribution function (PDF) methods, or diffraction data analysis. Experience with automated data pipelines, high-throughput analysis
-
financial models. The position will include the analysis of hydropower operation and expansion, optimization and equilibrium, market penetration, and interdependencies. This description documents the general
-
Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
The Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invites outstanding candidates to apply for a postdoctoral position in the area of uncertainty quantification and
-
, SuperPro Designer) and techno-economic analysis of biofuels, bioproducts, and biomaterials. Demonstrated ability to work effectively in a multidisciplinary environment. Strong publication record in peer
-
venues Position Requirements Required skills and qualifications: A PhD degree completed within the last 0-5 years (or soon to be completed) in numerical analysis, applied mathematics, computational science
-
We invite applications for a postdoctoral position in the Functional Coatings Group in the Applied Materials Division at Argonne National Laboratory to conduct advanced research in energy storage. A
-
The Cosmological Physics and Advanced Computing (CPAC) group at Argonne National Laboratory invites applications for a postdoctoral researcher to work closely with Dr. Lindsey Bleem
-
terms of universal computational skills. We encourage you to describe your work using broader technical terms—such as "statistical data analysis," "workflow automation," or "algorithm optimization"—rather
-
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