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Ying Li

2 months ago

Postdoctoral Researcher Position in Machine-Learned Interatomic Potentials for Hybrid Organic–Inorganic Perovskites University of Wisconsin-Madison in United States

Degree Level

Postdoc

Field of study

Computer Science

Funding

Postdoctoral researcher position; funding details, stipend, and duration are not specified in the post.

Country flag

Country

United States

University

University of Wisconsin-Madison

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Keywords

Computer Science
Materials Science
Molecular Dynamics
Density Functional Theory
Piezoelectricity
Atomistic Simulation
Physics
Computational Materials Science
ML

About this position

University of Wisconsin-Madison is seeking a postdoctoral researcher in the group of Ying Li (Associate Professor) to work on machine-learned interatomic potentials (MLIPs) for hybrid organic–inorganic perovskites (HOIPs).

The project combines MLIPs, density-functional theory (DFT), molecular dynamics, and machine learning to study finite-temperature phase behavior, structural dynamics, and electromechanical properties. The post mentions that the work builds on recent DFT–ML research on piezoelectric HOIPs.

This opportunity is a strong fit for candidates with training in computational materials science, atomistic simulation, DFT, molecular dynamics, perovskites, or machine learning. Applicants should have hands-on experience developing, validating, and deploying MLIPs.

To apply, send a CV and a short note describing your research interests and relevant experience to [email protected]. No deadline or funding details are stated in the post.

Institution: University of Wisconsin-Madison, Mechanical Engineering / College of Engineering.

Funding details

Postdoctoral researcher position; funding details, stipend, and duration are not specified in the post.

What's required

Candidates should have a background in computational materials science, atomistic simulation, density-functional theory, molecular dynamics, perovskites, or machine learning. Hands-on experience developing, validating, and deploying machine-learned interatomic potentials is specifically preferred.

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Professors

Associate Professor

University Name
Country flag

United States

Research Interests

Polymer Chemistry

Polymer Physics

Nanofiltration

Soft Matter Physics

Membrane Separation