The PhD thesis will be carried out at the CRISMAT Laboratory (UMR 6508, Caen), a joint research unit of CNRS, ENSICAEN, and the University of Normandy, comprising around one hundred researchers, faculty members, engineers, PhD students, and technical staff.
The project will be supervised by Sylvain Le Tonquesse and Franck Gascoin.
The contract is expected to start between September 1st and October 1st, 2025, for a duration of three years. This PhD project is part of the ANR "TEXMAG" project (2025–2029).

The PhD candidate will develop new textured magnetocaloric materials through novel synthesis methods, characterize their structure and physical properties, and apply artificial intelligence techniques to optimize their composition and processing conditions.
The development of new cooling technologies is a major challenge today in addressing environmental and energy issues. Traditional vapor compression-based systems use harmful refrigerants and show limitations in terms of durability and efficiency. Magnetocaloric refrigeration, particularly the rotational magnetocaloric effect (RMCE), offers a promising alternative for designing more sustainable and efficient devices. In this context, the PhD candidate will work on the development of new magnetocaloric materials exploiting the RMCE. He/she will conduct exploratory research on innovative intermetallic compounds, primarily 3d transition metal phosphides, selected for their abundance, complex anisotropic crystal structures, and interesting magnetic properties. He/she will develop an original synthesis method based on the magnetic slip casting process, enabling the preparation of textured polycrystals suitable for measuring RMCE properties. He/she will optimize the fabrication and heat treatment parameters to maximize the structural and magnetic quality of the materials. In parallel, he/she will implement machine learning techniques, particularly Bayesian optimization, to accelerate the discovery of new compositions and synthesis conditions. He/she will carry out magnetic characterizations to evaluate the magnetocaloric performance of the materials and contribute to modeling their behavior. Particular attention will be paid to the mechanical robustness and industrial scalability of the developed materials. This interdisciplinary project will combine advanced experimentation, artificial intelligence, and in-depth physical understanding to promote sustainable cooling solutions for the future.

The PhD candidate will be involved in the synthesis and study of new magnetocaloric materials. He/she will conduct syntheses in a glove box to handle air-sensitive precursors, using solid-state reactions in sealed tubes, mechanical milling, and SPS sintering. Characterization will primarily rely on powder and single-crystal X-ray diffraction to assess purity and solve new structures. Advanced diffraction campaigns (synchrotron or neutron) may also be conducted to deepen structural and magnetic analysis. The candidate will also use electron microscopy techniques (SEM and TEM) to analyze chemical compositions and local structures. He/she will develop expertise in measuring magnetic and physical properties using SQUID magnetometers and PPMS systems. The project involves the application of artificial intelligence methods (Bayesian optimization) using Python and libraries such as Scikit-learn, GPyTorch, and BoTorch. The PhD candidate will be trained in the critical analysis of experimental results and the optimization of synthesis conditions. He/she will also develop scientific communication skills through the writing of articles and the presentation of results at international conferences. This project thus offers comprehensive training combining materials synthesis, advanced characterization, and the application of modern digital tools.