Sort by
Refine Your Search
-
novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
-
). This position focuses on The investigation of coupled land-river-ocean processes in coastal flooding applications by developing a coupled E3SM configuration that incorporates a subgrid-scale version of the MPAS
-
insights and develop reduced order models (ROMs) for boundary layer flows and turbulent combustion. Integrate ROMs with CFD solvers and demonstrate predictive accuracy compared to traditional modeling
-
with this group to evaluate AERIS at S2S scales, couple ocean component to the model, data assimilation and regional refinement. In particular, this position will utilize generative AI to create a
-
primary goal of this work is aimed at advancing next-generation, lithium-ion technology through a detailed understanding and mitigation of surface degradation mechanisms that limit state-of-the-art lithium
-
The Theory Group in the Physics Division at Argonne National Laboratory is now seeking candidates for postdoctoral positions in nuclear theory, to begin as early as Spring 2026. The positions