Sort by
Refine Your Search
-
choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security
-
, LeafWeb, Sapfluxnet, PSInet) to translate trait variation into model parameter priors and functional constraints, and to explore parameter relationships with environmental conditions Hybrid modeling
-
. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially extensions to spinful
-
another, work together, and measure success. Basic Qualifications: A PhD related to computational or theoretical condensed matter physics, theoretical chemistry, theoretical materials science, or other
-
processing (e.g., arc melting, heat treatment) and relate processing to performance. Apply analytical and modeling approaches to interpret experimental results. Collaborate with internal and external research
-
in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
-
Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
another, work together, and measure success. Basic Qualifications: A PhD in Physics, Materials Science, Chemistry, or closely related field completed within the last 5 years. Sound understanding of advanced