Sort by
Refine Your Search
-
Computing Methods for Physical Sciences Section in CSED. The MsM group is focused on delivering multiscale, multi-fidelity computational models and systems using algorithms and analytics for materials and
-
opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: PhD in chemical engineering, chemistry, mechanical engineering, civil
-
Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI workflows for grid
-
analytical and modeling approaches to interpret experimental results. Collaborate with internal and external research teams to define and advance research directions. Present research findings and publish
-
methods. Additionally, you will develop/refine finite element and numerical analysis models to portray shear strength response as a function of temperature, and to attempt to ultimately reconcile those
-
to post-process characterization. This environment enables the creation of high-fidelity digital twins and AI-ready datasets that support real-time monitoring, predictive modeling, and process optimization
-
respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD degree in material science, chemical engineering, mechanical engineering, polymer chemistry
-
AI processes (e.g., model training, inference). Develop agentic AI systems and AI harnessing techniques to enhance model quality, resource optimization, and adaptive execution in diverse workflows
-
, instrumentation, and data acquisition systems; conduct laboratory and field testing; and perform thermodynamic analysis, system modeling, and performance assessments. Analyze and interpret experimental and modeling
-
. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially extensions to spinful