Postdoctoral Fellow: ML-Driven Catalysis & Design

University of Pittsburgh; Pittsburgh (Allegheny County); On-site; USD 52,000 - 68,000; Full time

Job summary

The University of Pittsburgh's Peng Liu group in the Department of Chemistry seeks a postdoctoral scholar in computational chemistry. The position focuses on applying broad computational chemistry and machine learning tools to study catalytic organic reactions and to develop predictive models for catalyst and reaction design.

The successful candidate will collaborate with experimental groups, contribute to peer‑reviewed publications, and advance method development with a strong emphasis on

Qualifications

• PhD in Computational Chemistry, Organic Chemistry, or a related discipline.
• Strong background in electronic structure calculations and/or molecular dynamics simulations.
• Experience in developing data-driven models and/or machine learning algorithms.
• Experience in collaborations with experimental organic research groups.
• Expertise in using a variety of computational chemistry software packages.
• Demonstrated competence in computational chemistry and organic chemistry.
• Excellent communication skills, including the ability to write peer-reviewed journal articles, present research findings to experimental collaborators, and create and give oral presentations at scientific conferences.

Responsibilities

• Use computational chemistry and ML tools to study mechanisms of catalytic organic reactions.
• Develop predictive models for catalyst and reaction design.
• Collaborate with experimental groups and share findings.

Job description

The University of Pittsburgh's Peng Liu group in the Department of Chemistry seeks a postdoctoral scholar in computational chemistry. The position focuses on applying broad computational chemistry and machine learning tools to study catalytic organic reactions and to develop predictive models for catalyst and reaction design.

The successful candidate will collaborate with experimental groups, contribute to peer‑reviewed publications, and advance method development with a strong emphasis on