• Durée: 6 mois
  • Niveau de recrutement: Master
  • Date de recrutement/début du poste: 2026-01-01
  • Laboratoire/Institution: LEMTA and LRGP, University of Lorraine
  • Nom du contact: Jean-Christophe Perrin et Cécile Lemaitre
  • Détail de l'offre: M2-Internship_MRI_Emulsion_Jan2026_FINAL.pdf

The internship will take place at the University of Lorraine (60,000 students) in Nancy, within two research laboratories LEMTA and LRGP.

The LEMTA (Laboratoire Énergies & Mécanique Théorique et Appliquée) has about 200 staff, including 80 professors and researchers, and specializes in heat transfer, fluid mechanics, and materials science, with applications in energy and complex systems engineering. The internship will take place within the "MRI for Engineering" team of LEMTA.

The LRGP (Laboratoire Réactions et Génie des Procédés) specializes in chemical and biological reaction engineering, as well as the modelling and optimization of processes for energy, environment, and industry, with over 300 staff members, including 100 professors and researchers. The intern will join the Product Engineering team, which focuses on the formulation, characterization, and modelling of complex fluids, such as emulsions.

The internship will start between January and March 2026 and last 6 months with a gratification of €670 per month. The estimated cost of living in Nancy (including accommodation, food, and social security) is around €900 per month.

Nancy (population 300,000, including 40,000 students) is a vibrant university city in eastern France, renowned for its Art Nouveau heritage and located just 1 hour 30 minutes from Paris by train.

To apply for the internship, send your CV and cover letter to
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This email address is being protected from spambots. You need JavaScript enabled to view it.

Candidates will be good students of engineering or basic science (chemical engineering, energy, physics, fluid mechanics, chemistry, biology, etc.) with an interest in spectroscopic methods such as NMR and image analysis. The candidate should be comfortable both with experiments and modelling. Skills of signal processing and programming would be highly appreciated.

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