Model

What Your Job Will Be Like :

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. The selected applicant must live within a reasonable distance for commuting to the assigned work location when necessary.

On any given day, you may be called on to :

• Demonstrate the creativity and know-how to (1) develop new materials’ featurization strategies and (2) adapt state-of-the-art machine and deep learning techniques to directly predict materials properties that are not achievable within currently established methodologies
• Use Density Functional Theory (or other first principles techniques) to acquire any data necessary to train such models when it does not already exist
• Integrate the outputs of machine learning models with other physics-based models or classical simulations to predict the thermodynamic, kinetic, or electronic properties of materials
• Collaborate with a diverse team of experimentalists and theorists to publish and present high quality research

Qualifications We Require :

• Ph.D. in materials science, chemistry, physics, materials engineering, or closely related field
• A record of scientific research accomplishment, as demonstrated by authorship of publications in peer-reviewed scientific journals that appear on your CV
• Research emphasis of applying theory, computational methods (such as density functional theory, ab initio quantum chemistry, and / or atomistic modeling), and / or machine learning to address materials science problems, as documented on your CV

Qualifications We Desire :

Exceptional applicants will also have previous experience and demonstrated success in some of the following :

• High-throughput electronic structure calculations of inorganic materials
• Open-source materials science libraries and databases like atomate, ase, pymatgen, Materials Project, OQMD, etc.
• Interpretable machine learning techniques to discover new structure-function relationships from experimental or computational data, which will in-turn be used to design novel materials with properties and behavior that are enhanced over existing options
• Active learning techniques to iteratively discover better materials
• Extensive collaboration skills within a larger team engaged in modeling, material synthesis, and use of advanced characterization tools
• Playing a major role in discovery and advancing the fundamental understanding of new materials through state-of-the-art computational and data science tools

Last updated : 2024-05-16