Cristallisation dirigée par ensemencement Optimisée par Simulations in Silico

The SMS Laboratory at the University of Rouen Normandie is seeking a motivated PhD candidate for the CRONOSS project, which aims to revolutionize crystallization control by combining in silico simulations with experimental validation. The successful applicant will develop a predictive pipeline using atomistic modeling and machine learning to identify optimal "seeds" for directing crystal growth, followed by rigorous experimental testing on pharmaceutical and functional materials. This role requires a strong background in Material Science, Physics, or Chemistry, coupled with a genuine interest in bridging computational and experimental work. The candidate will tackle complex challenges such as polymorphism in drugs and the synthesis of luminescent co-crystals, contributing to a novel methodology for tailoring material properties. Supervision is provided by Ivo Rietveld and Clément Brandel, with access to international research networks and advanced characterization facilities. Please note that the funding for this project is not yet confirmed.

Founded in 1966, the University of Rouen Normandie is a major public research institution located in the heart of Normandy, France. With approximately 22,000 students and 2,000 staff members, it is a multidisciplinary university covering sciences, humanities, law, economics, and technology. The university is deeply integrated into the regional economy, particularly in the pharmaceutical and chemical sectors, and is committed to innovation, international collaboration, and sustainable development.

The SMS laboratory (Sciences et Méthodes Séparatives, UR 3233) is a leading research unit hosted by the University of Rouen, specializing in the fundamental and applied study of separation processes and crystallization. The team is renowned for its expertise in controlling solid-state properties, polymorphism, and chiral resolution, using advanced experimental techniques (XRD, VCD, DSC). As a key player in the European research networks BEST-CSP and INGENIUM, SMS bridges the gap between academic discovery and industrial application, particularly for the pharmaceutical and fine chemical industries.

We are looking for a highly motivated PhD candidate with a Master’s degree in Chemistry, Physics, Materials Science, or Chemical Engineering. The perfect applicant possesses a genuine curiosity about the intersection of computational modeling and experimental synthesis. We welcome applicants from either a strong computational background (experience with molecular dynamics, DFT, or machine learning) or a robust experimental background (crystallization, X-ray diffraction, spectroscopy). You are driven by the challenge of solving complex "real-world" problems, such as optimizing drug solubility or designing luminescent materials, and you see value in using computer simulations to guide physical experiments.

Skills: A solid foundation in thermodynamics and crystallography is essential. Most importantly, you should be eager to collaborate with international experts and comfortable working in a multidisciplinary environment where theory meets practice.