PhD in Machine Learning for Computational Engineering and Materials Science Leuphana University Lüneburg in Germany

This PhD opportunity at Leuphana University Lüneburg, supervised by Professor Noomane Ben Khalifa and supported by Dr.-Ing. Frederic Bock, focuses on the development of advanced machine learning models for computational engineering and materials science. The project aims to create clustering, classification, regression, and reinforcement learning algorithms that can work with, enhance, or replace established computational methods such as the finite element method. By representing and exploiting relationships along the composition-process-structure-property-performance chain, the research will enable greater stability and control in novel manufacturing processes and help achieve desired properties in materials engineering.

Use cases will be defined within various manufacturing techniques for lightweight structures, supporting the development of new materials and innovative process design. The project seeks to combine computer simulations and machine learning to address complex problems in mechanical engineering and materials research that are currently unsolvable using only classical mechanistic modeling or traditional machine learning approaches.

The position is open to candidates with a strong background in computer science, mechanical engineering, materials science, or related fields. Experience with machine learning, computational modeling, or simulation is highly desirable. The university encourages applications from qualified women and individuals with disabilities, in line with its commitment to equal opportunity.

Applicants should prepare a comprehensive application package including a cover letter, CV, transcripts, and certificates, and submit it via the official application link by January 27th, 2026, referencing the position number 2026/WD 1 (943). This is an excellent opportunity for those interested in interdisciplinary research at the intersection of machine learning, computational engineering, and materials science, with the potential to impact the development of novel materials and manufacturing processes.