PhD Positions in Data Assimilation, Machine Learning, and Computational Fluid Dynamics for Aerospace Engineering Centrale Lille in France

Four PhD positions are available in France for students and researchers interested in Data Assimilation, Machine Learning, and Computational Fluid Dynamics (CFD), with a focus on aerospace engineering applications. The positions include one PhD on AI-assisted turbulence modeling for space turbopumps, in collaboration with M2N-CNAM, ONERA, and CNES, and three PhD positions on Data Assimilation applied to atmospheric reentry, in collaboration with LMFL-ENSAM, Rtech, and CNES. These opportunities offer strong collaborations among academia, research institutes, and industry partners.

The research areas span advanced topics such as turbulence modeling, atmospheric reentry, and the integration of artificial intelligence with fluid dynamics. Candidates will benefit from working in a multidisciplinary environment and engaging with leading French institutions such as Centrale Lille and its partners. The positions are ideal for those with a background in aerospace engineering, computer science, mechanical engineering, or physics, and who are interested in cutting-edge research at the intersection of these fields.

Applicants should have a relevant master's degree and experience in one or more of the following: data assimilation, machine learning, computational fluid dynamics, turbulence modeling, or aerospace engineering. For detailed requirements, funding information, and application instructions, candidates are encouraged to consult the full position descriptions available via the provided link.

These PhD positions offer a unique opportunity to contribute to innovative research projects with real-world applications in aerospace technology and atmospheric science. Interested candidates should review the full descriptions and follow the application procedures as outlined.