Noomane Ben Khalifa
2 months ago
PhD Position in Physics-Based Machine Learning Modeling for Materials and Process Design Helmholtz-Zentrum Hereon in Germany
Degree Level
PhD
Field of study
Computer Science
Funding
Full funding availableDeadline
December 31, 2026Country
Germany
University
Helmholtz-Zentrum Hereon

How do I apply for this?
Sign in for free to reveal details, requirements, and source links.
Where to contact
Keywords
About this position
The Helmholtz-Zentrum Hereon, a leading German research center, is offering a fully funded 4-year PhD position in the Institute of Material and Process Design, located in Geesthacht. This position focuses on the development of physics-based machine learning models for materials and process design, with a strong emphasis on integrating computational engineering and advanced simulation techniques. The research aims to create novel machine learning models for clustering, classification, regression, and reinforcement learning tasks, enhancing or replacing traditional computational methods such as the finite element method. The ultimate goal is to better understand and control the composition-process-structure-property-performance chain in materials science, enabling the design of innovative materials and manufacturing processes, particularly for lightweight structures.
The PhD project will be supervised by Prof. Noomane Ben Khalifa (Hereon/Leuphana University Lüneburg) and supported by Dr.-Ing. Frederic Bock (Hereon). The research will combine computer simulations and machine learning to address complex problems in mechanical engineering and materials research that are not tractable with classical approaches alone. The project will involve the development of machine learning pipelines in Python (using libraries such as PyTorch), data assimilation with experimental measurements, and the application of Explainable AI techniques to facilitate new scientific discoveries. Collaboration with cross-disciplinary colleagues and validation of results in various application areas are integral parts of the project. The successful candidate will be expected to publish and present research findings in international journals and conferences.
Applicants should possess a master’s degree in mechanical engineering, materials science, computational engineering, computer science, applied mathematics, physics, or a related field. Strong programming skills in Python and experience with neural networks and machine learning libraries (e.g., PyTorch) are required. Additional background in computational mechanics and applied mathematics is preferred. Proficiency in English, both spoken and written, is essential.
The position offers a dynamic and international research environment with around 1,000 employees from over 60 nations. Benefits include social benefits and remuneration up to pay group 13 according to TV EntgO Bund, a well-connected research campus, opportunities for further training, flexible work arrangements, a PhD Buddy Program, family-friendly policies, and access to excellent technical infrastructure. The application deadline is January 27th, 2026, and the position is set to commence on February 1st, 2026. Interested candidates should submit a comprehensive application (cover letter, CV, transcripts, certificates) via the online portal, referencing code 2026/WD 1.
For more information and to apply, visit the official application portal or the Helmholtz-Zentrum Hereon website. The institution is committed to equal opportunity and encourages applications from qualified women and individuals with disabilities.
Funding details
Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.
How to apply
Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.
Ask ApplyKite AI
Professors

How do I apply for this?
Sign in for free to reveal details, requirements, and source links.