PhD position on physics-based machine learning modeling for materials and process design
PhD position on physics-based machine learning modeling for materials and process design
PhD position on physics-based machine learning modeling for materials and process design
PhD position on physics-based machine learning modeling for materials and process design
Helmholtz-Zentrum Hereon
Forschung
Geesthacht
- Art der Beschäftigung: Vollzeit
- 63.500 € – 84.000 € (von XING geschätzt)
- Vor Ort
- Zu den Ersten gehören
PhD position on physics-based machine learning modeling for materials and process design
Über diesen Job
PhD position on physics-based machine learning modeling for materials and process design
| Beginn der Ausschreibung | 14.01.2026 |
| Ende der Ausschreibung | 27.01.2026 |
| Institut bzw. Bereich | Institut für Material- und Prozessdesign |
| Standort | Geesthacht |
Zum Bewerberportal
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) 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.
Your tasks
- 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 assimilation towards experimental measurements under consideration of uncertainties
- utilization of Explainable AI techniques to enable novel scientific discoveries
- 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
Your profile
- 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
- preferably also background knowledge in computational mechanics and applied mathematics
- highly proficient in spoken and written English
We offer you
- an exciting and varied job in a research centre with around 1,000 employees from more than 60 nations
- a well-connected research campus (public transport) and best networking opportunities, subsidy for the Deutschlandticket if certain conditions are met (job ticket)
- individual opportunities for further training
- social benefits according to the collective agreement of the public service and remuneration up to pay group 13 according to TV EntgO Bund
- an excellent technical infrastructure and modern workplace equipment
- 6 weeks holiday per year; company holidays between Christmas and New Year's Day
- very good compatibility of private and professional life; offers of mobile and flexible work
- PhD Buddy Program
- family-friendly company policy with childcare facilities, e.g. nursery close to the company
- free assistance program for employees (EAP)
- corporate benefits
- a varied offer in the canteen on campus
Severely disabled persons and those equaling severely disabled persons who are equally suitable for the position will be considered preferentially within the framework of legal requirements.
Interested?
Then we are looking forward to receiving your comprehensive application documents (cover letter, CV, transcripts, certificates etc.) indicating the reference number 2026/WD 1 (943) until January 27th, 2026.
