PhD position - Predicting the stress field in atherosclerotic plaque of arteries using scientif[...]

Forschungszentrum Jülich; Jülich; Hybrid; EUR 35.000 - 54.000; Vollzeit

Zusammenfassung

A leading research center in Germany is seeking a PhD student to develop learning-based surrogate models for predicting stress fields in patient-specific arteries. The role involves enhancing models using physics, utilizing large language models, and collaborating with cardiovascular simulation experts. Ideal candidates have a degree in applied mathematics, programming skills in Python and C/C+, and experience in machine learning. The position offers opportunities for conferences and training in a supportive international environment.

Leistungen

30 days of annual leave

Flexible working arrangements

Continuous scientific mentoring

Participation in conferences

Comprehensive training program

Qualifikationen

• Genuine interest in data science and relevant application domains.
• Excellent programming skills in Python and C/C++.
• Good experience in machine learning and parallel computing.
• Experience with deep learning frameworks like Tensorflow or Pytorch is advantageous.
• Working proficiency in English and knowledge of German is beneficial.

Aufgaben

• Develop and compare data-driven models for stress prediction.
• Enhance models using various physics-aware machine learning techniques.
• Utilize large language models for neural network design.
• Present research results at conferences in Germany and abroad.
• Prepare scientific publications and project reports.

Kenntnisse

Python

C/C++

Machine learning

Parallel computing

Effective communication

Ausbildung

University degree in applied mathematics or computational engineering

Tools

Tensorflow

Pytorch

Jobbeschreibung

Your Job

We are looking for a PhD student to develop learning‑based surrogate models for predicting stress fields in patient‑specific arteries. Especially high stresses in plaque can lead to rupture, which is one cause of a stroke and thus the prediction of plaque rupture is very relevant. The steps in the development of surrogate models are building data‑driven models from medical imaging, extending them with physics‑based approaches, and adapting existing physics‑integrated neural network approaches for stress prediction in arterial walls and plaque. Another part of the project is exploring the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning.

Your tasks

• Development and comparison of data‑driven models for the prediction of stresses in arterial walls and plaque
• Enhancing the models with physics, using different physics‑aware machine learning models from the field of scientific machine learning
• Exploiting large language models to support neural network design and data preprocessing
• Participation in conferences in Germany and abroad (incl. presenting your research results)
• Preparing scientific publications and project reports

Your Profile

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 (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

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 exclusion required)

Knowledge of German is beneficial

Our Offer

We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! We offer ideal conditions for you to complete your doctoral degree :

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)

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, 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 :

Targeted services for international employees, 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 :