Computational Materials Scientist - Postdoctoral Researcher
Core
Develop multicomponent thermodynamic and kinetic databases and integrate ICME microstructure evolution models to screen promising alloy compositions for extreme environments using high-performance computing.
Role type
Postdoctoral Researcher in Computational Materials Science
Builds
Multicomponent thermodynamic/kinetic databases and ICME frameworks for alloy design
Domain
National Security / Computational Materials Science / Metallurgy
Deliverable
production ML models | research
Required skills
CALPHAD, microstructure and property modeling, uncertainty quantification, ICME, alloy design, design of experiments, thermodynamics, phase stability, phase transformations, defect structures, solidification, thermo-mechanical processing
Preferred skills
artificial intelligence, machine learning, Bayesian Optimization, black-box optimization, gradient-based optimization, parameterizing numerical methods for phase transformations, alloy synthesis, processing, and characterization
Technologies
Thermo-Calc, Pandat, FactSage, PyCalphad, Thermochimica, OpenCalphad, High Performance Computing
Responsibilities
Independently develop multicomponent thermodynamic and kinetic databases; Incorporate ICME microstructure evolution models and property predictions into the Materials Acceleration Platform; Develop uncertainty quantification and propagation methods; Interface with experimentalists in design of experiment and material development campaigns; Publish research results in peer-reviewed scientific journals and present at external conferences.
Seniority
Postdoctoral Researcher