
We have immediate openings
for students and postdoctoral scholars
Join our Team!
Graduate Students: Note that you must first be admitted to the UM College of Engineering.
Postdoctoral Candidates: Please include a cover letter detailing your research experience, skills, interest in joining, and a CV. Recommendations may also be requested.
Available Projects

Simulation-Guided Optimization of Novel Membranes
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Openings: 1 Student, 1 Postdoc
Overview
In this Department of Energy funded project, we will contribute to design of next-generation membranes enabling sustainable ammonia production. Reactive ChIMES atomistic simulations will be used to predict zeolite nanosheet complexing and to determine how multi-nanosheet gallery structures govern emergent guest molecule transport and loading selectivity. Efforts will be conducted closely with experimental collaborators, to inform design and synthesis of new high performance membranes.

Demystifying Shock-Synthesis of Nanomaterials
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Openings: 1 Student, 1 Postdoc
Overview
In this National Nuclear Security Administration funded project, we will deploy reactive ChIMES atomistic simulations to elucidate the how shockwaves can be used to synthesize nanocarbon materials of broad technological and scientific utility. Through our simulations, we aim to reveal the complex interplay between precursor chemical composition, shock and release profiles, and emergent nanomaterials. This work will be conducted in close collaboration with theoretical and experimental scientists at Lawrence Livermore National Laboratory

Enabling Quantum Accurate Simulation on Large Scales with Artificial Intelligence
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Openings: 1 Student, 1 Postdoc
Overview
In this National Nuclear Security Administration funded project, we will radically expand the machine learning framework underlying the ChIMES reactive interatomic framework. Through these efforts, we will build out artificial intelligence-driven tools automating generation and validation of ChIMES models for arbitrary applications in materials modeling, and establish general guiding principles for quantifying uncertainty in machine learned interatomic models. This work will be conducted in close collaboration with both external researchers and those within the UM Chemical Engineering Department.