M2 Internship in AI-Assisted Atomistic Simulations of Heat Transfer Nanofluids (Computational Chemistry/Physics)

This M2 internship at Université de Lorraine offers a unique opportunity for students in Computational Chemistry or Physics to engage in cutting-edge research on AI-assisted atomistic simulations of heat transfer nanofluids. The project focuses on the development of AI-enhanced molecular dynamics simulations to study deep eutectic solvent (DES) nanofluids and their structure–property relationships in heat-transfer systems. By combining machine-learning-trained interaction models with experimental data, the research aims to design next-generation sustainable heat-transfer nanofluids with improved thermal conductivity and performance. Interns will work at the LPCT and LEMTA laboratories, collaborating with Prof. Francesca Ingrosso and Dr. Mykola Isaiev. The research involves advanced computational techniques, including molecular dynamics, statistical mechanics, and programming in Python or Fortran. The project is ideal for students interested in the intersection of chemistry, physics, materials science, and computer science, especially those keen on applying AI and machine learning to real-world scientific problems. Applicants should have a strong academic background, proficiency in English, and hands-on experience with molecular dynamics simulations and programming. The internship may serve as a stepping stone to a fully funded PhD position, with grants potentially available from October 2026 for a three-year doctoral program at Université de Lorraine. Interested candidates are encouraged to contact the supervisors directly via email, providing their CV, academic transcripts, and a motivation letter outlining their relevant skills and research interests. This opportunity is particularly suited for students passionate about sustainable energy, nanofluid technology, and computational modeling, offering exposure to interdisciplinary research and collaboration within a leading French research institution.