Khalid Ferji is Full Professor at Université de Lorraine, with appointments at EEIGM and ENSIC, and conducts his research at LRGP. His activities lie at the interface of polymer chemistry, materials science, and artificial intelligence.
His scientific expertise spans controlled radical polymerization, polymer self-assembly, polysaccharide-based nanomaterials, and photo-RAFT polymerization-induced self-assembly (PISA). He obtained his PhD in Polymer Chemistry in 2013 and his Habilitation (HDR) in 2022.
Over the past decade, his research has contributed to the development of polysaccharide-based nanocarriers, light-mediated polymerization strategies, and self-assembled polymer systems. More recently, he has developed a new research direction in AI-driven polymer science, exploring how machine learning and data-driven approaches can better represent complex polymer systems, exploit experimental and characterization data, predict material properties, and assist in the discovery and design of new materials.
Beyond his research activities, Khalid serves as pedagogical coordinator of the Master’s program CHIPS at Université de Lorraine. He is also President of the Eastern Section of the French Polymer Group (GFP), contributing to the activities and visibility of the polymer community.
Beyond his research activities, Khalid serves as pedagogical coordinator of the Master’s program CHIPS at Université de Lorraine. He is also President of the Eastern Section of the French Polymer Group (GFP), contributing to the activities and visibility of the polymer community.
His long-term research vision is to strengthen the integration of polymer chemistry, artificial intelligence, and process engineering to accelerate the discovery, understanding, and sustainable development of polymer materials.
Research Themes
- AI-driven polymer informatics – Digital representations of polymers, machine learning for property prediction, characterization data analysis, and AI-assisted materials discovery and inverse design.
- Polymer synthesis and self-assembly – Design of functional polymer materials through controlled radical polymerization, photo-RAFT, and polymerization-induced self-assembly (PISA).
- Physico-chemistry and characterization of polymers – Polymer morphology, colloidal systems, structure–property relationships, and advanced characterization.
- AI-assisted experimentation and process engineering – Integration of machine learning, experimental automation, and process engineering toward data-driven polymer development and scale-up.