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Postdoctoral Appointee: Materials Models And Machine Learning for Energy Sustainability (Hybrid)
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Full-time
- What Your Job Will Be Like: We are seeking a Postdoctoral Appointee/Scientist to apply electronic structure calculations, machine learning, and/or first principles simulations to accelerate materials discovery.
- Research areas may broadly span inorganic materials modeling (oxides, nitrides, MXenes, transition metal dichalcogenides, or others) for energy and fuels (hydrogen or ammonia production, storage, or utilization), carbon capture, or other applications.
- A central theme in this effort is the rational design of new materials by changing the chemical composition, the arrangement of the atoms or molecules in crystalline or amorphous configurations, and the size, shape, and orientation of nanoparticles, films, crystals, or other nano- or macroscopic units.
- Excellent communication skills are required to convey results with multi-disciplinary scientific teams and help prioritize and direct experimental validation efforts.
- The selected applicant can work a combination of onsite and offsite work.
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