PhD in Frugal Machine Learning and Density Functional Theory for Sustainable Catalytic Materials

This doctoral position focuses on the intersection of machine learning and density functional theory to accelerate the discovery of sustainable catalytic materials. The successful candidate will work on developing 'frugal' machine learning models that require less computational power while maintaining high predictive accuracy for chemical properties. The project aims to address critical challenges in material design for green chemistry applications. Applicants are expected to have a strong background in computational chemistry or materials science and an interest in algorithmic development.

• Develop and implement machine learning algorithms for the prediction of catalytic properties.
• Perform high-throughput density functional theory calculations to generate training data.
• Optimize computational workflows to reduce the energy and time requirements of material screening.
• Analyze and interpret simulation results to guide the design of new catalytic materials.
• Collaborate with experimental teams to validate computational predictions.
• Document research findings and prepare manuscripts for publication in peer-reviewed journals.