Postdoc: Build ML Interatomic Potentials for Perovskites

ScholarLink Inc.; Madison (WI); On-site; USD 60,000 - 76,000; Full time

Job summary

University of Wisconsin–Madison invites applications for a Postdoctoral Researcher in Machine-Learned Interatomic Potentials for Hybrid Organic–Inorganic Perovskites. The position combines MLIPs, DFT, MD, and ML to study finite-temperature phase behavior and electromechanical properties.

Candidates should have hands-on experience developing, validating, and deploying MLIPs and a background in computational materials science or related fields.

Qualifications

• Hands-on experience developing, validating, and deploying machine-learned interatomic potentials.

Responsibilities

• Develop machine-learned interatomic potentials for HOIPs.
• Investigate finite-temperature phase behavior, structural dynamics, and electromechanical properties.

Skills

Hands-on experience developing inter-

Tools

Density Functional Theory (DFT)

Molecular Dynamics

Machine Learning

Job description

University of Wisconsin–Madison invites applications for a Postdoctoral Researcher in Machine-Learned Interatomic Potentials for Hybrid Organic–Inorganic Perovskites. The position combines MLIPs, DFT, MD, and ML to study finite-temperature phase behavior and electromechanical properties.

Candidates should have hands-on experience developing, validating, and deploying MLIPs and a background in computational materials science or related fields.