Computational Modeling - Postdoctoral Research Staff Member
Core
Develop integrated simulation-experiment workflows to optimize metal joining in alloyed systems using atomistic and multiscale modeling.
Role type
Postdoctoral Research Staff Member (Computational Modeling)
Builds
GPU-enabled Machine-learning interatomic potentials (MLIP) and first-principles simulation data for grain boundary and interface modeling.
Domain
Materials Science / Computational Physics / High-Performance Computing
Deliverable
production ML models
Required skills
Python, C++, C, Fortran, DFT, Molecular Dynamics, High-Performance Computing, Machine Learning, Interatomic Potentials, Electronic Structure Calculations
Preferred skills
Kinetic Monte Carlo, Enhanced Sampling, Multiscale Modeling Integration, AI/ML Surrogate Modeling, Bayesian Optimization, Uncertainty Quantification
Technologies
VASP, Quantum Espresso, GPU
Responsibilities
Perform and analyze first principles-based simulations to generate reference data; Develop GPU-enabled MLIP models for complex intermetallic systems; Provide inputs for mesoscale and thermodynamic modeling; Develop and maintain advanced atomistic simulation codes; Document research and publish in peer-reviewed journals.
Seniority
Postdoctoral (Early career, mentored)