Atomistic and Data-Driven Modeling of Materials for Energy Applications - Postdoctoral Researcher
Core
Conduct mentored research in atomistic and data-driven modeling of materials for energy applications, focusing on reactivity, transport, phase evolution, and degradation under operating conditions.
Role type
Postdoctoral Researcher (IC)
Builds
Physics-informed surrogate models, machine-learning interatomic potentials, and multiscale simulations for DOE energy projects.
Domain
Materials Science / Computational Chemistry / Energy Applications
Deliverable
production ML models | research
Required skills
density functional theory, molecular dynamics simulations, machine learning for materials, statistical analysis, kinetic Monte Carlo, phase-field modeling
Preferred skills
multiscale model integration, AI/ML-enabled materials discovery, Bayesian optimization, uncertainty quantification
Technologies
DFT, MD, HPC, ML frameworks, cluster expansion, reduced order models
Responsibilities
Perform electronic structure theory-based simulations of complex materials; conduct thermodynamic and kinetic analyses of reactions and phase transitions; develop machine-learning interatomic potentials and surrogate models; contribute to research planning and execution under mentorship; document research and present results at conferences.
Seniority
Postdoctoral (recent PhD, mentored IC)