PhD: Digital Twin Framework for Bioprocess Optimization Using AI and Hybrid Modeling

This PhD project focuses on creating advanced digital tools—combining machine learning, hybrid modeling, and reinforcement learning—to optimize bioprocesses. The research will integrate omics data, physical models, and data-driven approaches to predict and enhance metabolite production in microbial systems. The candidate will work within an interdisciplinary team across three INRAE laboratories, applying Design-Build-Test-Learn cycles to refine bioprocess efficiency. The position offers a salaried contract, full social benefits, and opportunities for international conference participation. The project aligns with INRAE’s mission to address global challenges in agriculture, food, and environmental sustainability.

• Develop AI-driven digital twin frameworks to simulate and optimize bioprocesses
• Integrate omics data, physical models, and machine learning for predictive modeling
• Design and implement reinforcement learning algorithms for process optimization
• Collaborate with interdisciplinary teams across three INRAE laboratories
• Conduct Design-Build-Test-Learn cycles to refine bioprocess efficiency
• Publish research findings in peer-reviewed journals and present at international conferences
• Contribute to the development of soft sensors and fault-detection systems for bioreactors
• Optimize metabolite production in microbial systems using hybrid modeling approaches