Sort by
Refine Your Search
-
Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
for model refinement. Perform multi-scale simulations (e.g. DFT / atomistic / phase-field simulations) to train AI/ML models. Conduct scientific research on ferroelectrics and/or 2D memristive materials
-
simulations for fermionic and Hubbard-like materials models • Collaborate within a multi-disciplinary research environment consisting of quantum computing experts, computational scientists, and condensed
-
models that can describe rates and locales of precipitation. Major Duties/Responsibilities: Conduct independent research using atomic-scale simulation with rare event methods to understand hydroxylation
-
Proficiency in the use of industry standard modeling and simulation tools, such as spreadsheet-based process cost modeling, input/output modeling, and commercially available life cycle analysis tools such as
-
and simulation tools, such as spreadsheet-based process cost modeling, input/output modeling, or commercially available life cycle analysis tools such as SimaPro and openLCA. Excellent written and oral
-
) for lattice model simulations. Experience working in a multi-disciplinary research environment. Demonstrated written and oral communication skills, a proven publication record, and effective interpersonal
-
applications, (2) design and architecture of integrated, hybrid, atomistic simulation software packages (e.g., LAMMPS) and DL models, and (3) documentation, verification and validation, and software quality
-
analysis by integrating diverse datasets (e.g., in situ observations, remote sensing products, model simulations) to inform model development, calibration, and validation. Collaborate with a
-
and transient inverter modeling and different applications of the simulation. Selection will be based on qualifications, relevant experience, skills, and education. You should be highly self-motivated
-
for simulating atomic nuclei, as well as preparing data and using machine learning models for investigating how the properties of atomic nuclei connect to fundamental questions in physics, such as constraining