Helmholtz-Zentrum Hereon

The Helmholtz-Zentrum Hereon conducts cutting-edge international research for a changing world: Around 1,000 employees contribute to the tackling of climate change, the sustainable use of the world's coastal systems and the resource-compatible enhancement of the quality of life. From fundamental research to practical applications, the interdisciplinary research spectrum covers a unique range.


Institute of Material and Process Design

The Institute of Material and Process Design is dedicated to the sustainable and ecological development of innovative materials and manufacturing processes, especially for the transportation sector and medical technology. To this end, materials are tailored and manufacturing processes are designed to conserve resources - from the modeling of material behavior to the development of the material to the finished component.


PhD position on physics-based machine learning modeling for materials and process design

Reference code: 980 - 2026/WD 1
Commencement date: as soon as possible
Work location: Geesthacht
Application deadline: April 26th, 2026


The Institute of Material and Process Design at the Helmholtz-Zentrum Hereon is offering a 4-year PhD position in the area of machine learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement tasks to work with, enhance or replace established methods from computational engineering and computer simulation (such as the finite element method and constitutive materials models ) to represent and exploit relationships along the composition-process-structure-property-performance chain; therefore, enable stability and control of novel manufacturing processes as well as achieving desired properties within materials science and engineering. Use cases will be defined within different manufacturing techniques of lightweight structures to enable novel development of materials and process design.


The PhD position will be supervised by Prof. Noomane Ben Khalifa (Hereon/Leuphana University Lüneburg) and supported by Dr.-Ing. Frederic Bock (Hereon).


The objective is to combine computer simulations and machine learning models to extend their compatibility with problems from mechanical engineering and materials research that could not be addressed so far due to their high complexity, which prevents approaches that solely rely on classical mechanistic modeling or classical machine learning.


Equal opportunity is an important part of our personnel policy. We would therefore strongly encourage qualified women to apply for the position.

development of novel machine models based on supervised, unsupervised and reinforcement learning that can be combined with, enhance or replace methods from computational engineering and computer simulation
data modeling and assimilation towards experimental measurements under consideration of uncertainties
utilization of Explainable AI techniques to enable novel scientific discoveries
explore and evaluate usability of system architectures such as Retrieval-Augmented Generation (RAG) for data retrieval and knowledge inference
implementation of your machine learning pipeline in Python (using e.g. PyTorch)
validation of your results in collaboration with colleagues from various application areas (cross-disciplinary)
publication and presentation of your scientific results in international scientific journals and at international conferences and workshops

master’s degree in mechanical engineering, materials science, computational engineering, computer science, applied mathematics, physics or a similar area
very good programming skills in Python
good prior experience with neural networks using common Python-ML libraries such as PyTorch
background knowledge in computational mechanics and materials science
highly proficient in spoken and written English

an exciting and varied job in a re...