Sort by
Refine Your Search
-
statistics and uncertainty quantification (Bayesian analysis, data assimilation, Gaussian process modeling), and their application to complex systems modeling. Experience in developing and applying statistical
-
organometallic / inorganic heterogeneous catalysis Design, synthesize, and characterize metal-ligand complexes supported on metal oxide and/or non-traditional support materials Investigate the catalytic activity
-
environmental analysis, and the ability to identify topics and data sources and engage experts to conduct research. Capable of identifying key features of complex systems and processes. Well-developed problem
-
transition metal complexes, donor-acceptor pairs, and hybrid molecular-material interfaces. By designing and deploying state-of-the-art ultrafast nonlinear optical spectroscopy techniques—such as transient
-
The Buildings & Industry Group within the Energy Systems and Infrastructure Analysis (ESIA) Division at Argonne is seeking to hire a postdoctoral appointee to conduct research and modeling-based
-
cases. This position is focused on advancing the capabilities of LLMs to address complex problems within specific scientific domains, with an emphasis on climate risk assessment and analysis. As part of a
-
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
-
of complex propulsion systems involving modeling of multi-phase flows, turbulent combustion, heat transfer, combustion, and thermo-mechanical fluid-structure interaction by further developing commercial/in
-
The Advanced Grid Modeling group at Argonne National Laboratory's Center for Energy, Environmental, and Economic Systems Analysis (CEEESA) is seeking a highly motivated Postdoctoral Researcher
-
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