FZ Juelich is looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation.
Your tasks:
- Development of physics-aware ML models for 3D blood-flow prediction
- Integration of domain decomposition methods into the learning framework to enable efficient model parallel training
- Implementation and optimization of GPU-accelerated training pipelines
- Validation of models on patient-specific geometries obtained from MRI data
- Participation in conferences in Germany and abroad (incl. presenting your research results)
- Preparing scientific publications and project reports
Table of Content
Summary
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Benefits
- Outstanding scientific and technical infrastructure
- Highly motivated groups as well as an international and interdisciplinary working environment at one of Europe’s largest research establishments
- Continuous scientific mentoring by your scientific advisors
- Chance of participating in (international) conferences
- Unique HDS-LEE graduate school program (including data science courses, soft skill courses and annual retreats) https://www.hds-lee.de/about/
- A qualification that is highly welcome in industry
- 30 days of annual leave and flexible working arrangements, including partial remote work
- Further development of your personal strengths, e.g. via a comprehensive training program; a structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors: https://www.fz-juelich.de/judocs
- Targeted services for international employees, e.g. through our International Advisory Service
The position is limited to three years, with a possible one-year extension. Pay is in line with 75% of pay group 13 of the Collective Agreement for the Public Service (TVöD-Bund) and additionally 60 % of a monthly salary as special payment („Christmas bonus“). The monthly salaries in euro can be found on the BMI website: https://go.fzj.de/bmi.tvoed.entgelt
Requirements
- Genuine interest in data science and one or more of its application domains: life and medical sciences, earth sciences, energy systems, or material sciences
- University degree (M.Sc. or equivalent) in applied mathematics or in computational engineering science, computer science, simulation science with a strong background in applied mathematics
- Excellent programming skills (Python, C/C++)
- Good experience in machine learning and parallel computing
- Good organisational skills and ability to work both independently and collaboratively
- Experience with deep learning frameworks, such as Tensorflow or Pytorch is advantageous
- Experience in numerical methods for partial differential equations is beneficial
- Effective communication skills and an interest in contributing to a highly international and interdisciplinary team
- Working proficiency in English for daily communication and professional contexts (TOEFL or equivalent or excemption required)
- Knowledge of German is beneficial
Check also:
2026 Fully Funded Humboldt Research Fellowship
2026 Eutopia PhD Co-tutelle Call
Application Deadline
Not SpecifiedHow To Apply
Are you qualified and interested in this opportunity? Kindly go to
Forschungszentrum Jülich on www.fz-juelich.de to apply
For more information, kindly visit FZ Juelich webpage.
