PhD: Efficient ML + DFT for Sustainable Catalysis

Univ. Lorraine CNRS; Nancy; On-site; EUR 20,000 - 26,000; Full time

Job summary

Université de Lorraine invites applications for a PhD project focused on Frugal Machine Learning and Density Functional Theory for sustainable catalytic materials. The study is based in Nancy (Grand Est, France) and combines CO2 hydrogenation modeling and advanced ML approaches to reduce data needs while maintaining accuracy.

The project explores transfer learning and machine-learning interaction potentials, collaborating with IJL and LORIA, with an international network including Jožef Stefan

Qualifications

• Strong background in chemistry, physical chemistry, materials science, or condensed matter physics.
• Experience in data science and Python programming is desirable.
• Excellent communication skills in English, both written and spoken.

Skills

Chemistry background

Data science

Python

High-performance computing

Quantum chemistry

English communication

Education

PhD in chemistry, materials science, or physics

Tools

Python

DFT/quantum chemistry packages

Job description

Université de Lorraine invites applications for a PhD project focused on Frugal Machine Learning and Density Functional Theory for sustainable catalytic materials. The study is based in Nancy (Grand Est, France) and combines CO2 hydrogenation modeling and advanced ML approaches to reduce data needs while maintaining accuracy.

The project explores transfer learning and machine-learning interaction potentials, collaborating with IJL and LORIA, with an international network including Jožef Stefan