Sort by
Refine Your Search
-
related field. Experience with finite element simulations and developing constitutive models. Knowledge of high temperature creep crack growth. Knowledge of engineering design codes such as the ASME Boiler
-
material property database for composites. The candidate will utilize the database to develop AI models for composite discovery. The candidate will work with a multidisciplinary team to set up finite element
-
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
-
for IDPs and enable new strategies in cancer research and treatment. A third component of the role will support ongoing laboratory studies examining the effects of low-dose radiation on human cells. Key
-
-principles and atomistic simulations with machine-learned interatomic potentials to: Model reaction pathways on metal-oxide surface, including adsorption, reactions and diffusion steps. Construct atomistic
-
specializing in energy economics and supply chain analysis. This role is pivotal in evaluating the economic competitiveness of the U.S. in the production and manufacturing of energy-related materials and
-
, materials science, or a related discipline Background and/or interest in one or more of the following areas: critical elements and materials, electrical double layer theory and applications, solid–liquid
-
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
-
applying machine learning or other elements of artificial intelligence to solving significant scientific or engineering problems Interest in software development, with particular emphasis on the Python
-
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