PhD Position in Computational Physics: Engineering New RubisCO Proteins

This PhD position at Paris Sciences et Lettres (PSL) focuses on revolutionizing photosynthetic carbon fixation by engineering novel RubisCO enzymes with enhanced CO₂ catalytic activity. The project combines statistical physics, generative AI, and directed evolution to overcome current RubisCO inefficiencies. The successful candidate will develop predictive models to navigate RubisCO’s sequence-function landscape, integrating bio-physical data, biological sequences, and experimental datasets from algal evolution. The work involves close collaboration with biochemistry and biology labs at Sorbonne Université and Institut Pasteur, including a 3-month secondment at the University of Chicago. The interdisciplinary approach bridges physics, biochemistry, synthetic biology, and AI, aiming to produce novel RubisCO variants validated experimentally. This project is part of the PRISM programme, co-funded by the European Union under Horizon Europe MSCA COFUND.

• Develop predictive models combining statistical physics, generative AI, and bio-physical data to navigate RubisCO’s sequence-function landscape.
• Design novel RubisCO variants using interpretable AI models and propose mutations to enhance catalytic performance.
• Collaborate with experimental partners to validate AI-designed RubisCO variants in Chlamydomonas algae and refine models accordingly.
• Conduct a 3-month secondment at the University of Chicago to integrate advanced methods for epistasis mapping and evolutionary landscape analysis.
• Integrate experimental datasets from algal evolution to estimate mutational effects on stability and catalytic efficiency.
• Publish findings in high-impact journals and present results at international conferences.
• Participate in interdisciplinary training courses on sustainability, innovation, entrepreneurship, and career development.