Peng Liu
4 days ago
Postdoctoral Scholar in Computational Chemistry and Machine Learning for Catalysis University of Pittsburgh in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Available
Country
United States
University
University of Pittsburgh

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About this position
Postdoctoral Scholar in Computational Chemistry and Machine Learning at the University of Pittsburgh.
The Peng Liu group is hiring a postdoctoral scholar to work on computational chemistry for catalytic organic reactions, with a strong emphasis on mechanism discovery and predictive catalyst/reaction design. The project combines transition-metal catalysis, organic reaction mechanisms, biocatalysis, and machine learning to study C–H/C–C activation, alkene functionalization, cross-coupling, ligand effects, radical and photocatalytic reactions, non-covalent interactions, catalyst flexibility, and dynamics in solution.
The successful candidate will use a broad computational toolkit including DFT, high-level ab initio methods, classical and ab initio molecular dynamics, enhanced sampling, QM/MM, machine-learned interatomic potentials, and graph neural networks. The work is supported by dedicated group HPC resources and is closely connected to experiments through collaborations with 50+ synthetic chemistry groups and industry partners.
Eligibility highlights: PhD in computational chemistry, organic chemistry, or a related field; strong background in electronic structure calculations and/or molecular dynamics; experience with ML/data-driven modeling, programming, and experimental collaboration is highly valued.
Application: submit a cover letter, CV with publication list, and reference contacts via the application link. A research statement is optional. Review begins immediately and continues until the position is filled.
Funding details
Available
What's required
PhD in computational chemistry, organic chemistry, or a related field. Strong background in electronic structure calculations and/or molecular dynamics simulations is required. Experience with machine learning or data-driven modeling, programming, and experimental collaborations is highly valued.
How to apply
Apply through the linked application portal. Submit a cover letter, CV with publication list, and reference contacts; a research statement is optional. Review begins immediately and continues until the position is filled.
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