Master's/Engineer Internship in Process Engineering and Data Sciences – PIML–TL Hybrid Model for Ammonia Production Kinetics

The Reactions and Process Engineering Laboratory (LRGP) at the University of Lorraine is offering a Master's or Engineer internship focused on the development of a Physics-Informed Machine Learning (PIML)–Transfer Learning (TL) hybrid model for predicting ammonia production kinetics. The internship is based at the Grandville site in Nancy, France, and is ideal for students specializing in Process Engineering or Data Sciences. The project involves tuning neural network hyperparameters, testing various modeling scenarios, evaluating multiple transfer learning methods, and validating the developed model using experimental results. This opportunity is particularly relevant for students interested in the intersection of chemical engineering, data science, and advanced modeling techniques. The internship provides a statutory allowance of approximately 550 euros net per month, with a start date in February or March 2026. Applicants should have a background in process engineering or data sciences, and familiarity with neural networks and transfer learning is highly desirable. The position is supervised by Xiaoqian Huang at the University of Lorraine, with the announcement shared by Assistant Professor Nidhal HNAIEN from the University of Monastir. To apply, interested candidates should contact Xiaoqian Huang via email at [email protected], including their CV and relevant academic records. This is an excellent opportunity for students seeking hands-on experience in cutting-edge research at the interface of engineering and data science.