PhD Researcher: AI for Personalised Fracture Risk

ETH Zurich; Lavamünd; Vor Ort; EUR 36.000 - 52.000; Vollzeit

Benefits dieser Stelle

TAFEP accreditation

Diverse workplace

25 days annual leave

Birthday Leave

Healthcare insurance

Hybrid work arrangement

NS mark certification

Zusammenfassung

ETH Zurich's Singapore-ETH Centre invites applications for a PhD Researcher role focused on AI-based medical image analysis to predict fracture risk. You will develop deep-learning models, extend them to longitudinal data, and validate results with biomechanical simulations, collaborating with researchers across disciplines.

The position emphasizes independent work, strong ML/DL foundations, and proficiency in Python and PyTorch, with opportunities for MSc/BSc supervision and flexible hybrid

Qualifikationen

• MSc degree in a quantitative field (CS/engineering/math/physics).
• Independent researcher capable of tackling complex topics.
• Strong background in ML/DL and medical image processing; Python and NN training/evaluation experience.
• Familiarity with biomechanics or finite-element modelling is a plus.
• Experience with parallel/high-performance computing and shared HPC clusters.
• Version control and collaborative development practices required.
• Proficient in written and spoken English.

Aufgaben

• Develop AI/deep-learning models to predict fracture risk from bone-density and CT images.
• Extend image-based models to longitudinal data for personalised trajectories.
• Validate predictions against biomechanical simulations and real-world outcomes.
• Co-supervise MSc and BSc students.

Kenntnisse

Machine learning

Deep learning

Medical image processing

Python programming

Neural networks

HPC

Software engineering practices

Ausbildung

MSc degree in a quantitative field

Tools

PyTorch

HPC clusters

Jobbeschreibung

ETH Zurich's Singapore-ETH Centre invites applications for a PhD Researcher role focused on AI-based medical image analysis to predict fracture risk. You will develop deep-learning models, extend them to longitudinal data, and validate results with biomechanical simulations, collaborating with researchers across disciplines.

The position emphasizes independent work, strong ML/DL foundations, and proficiency in Python and PyTorch, with opportunities for MSc/BSc supervision and flexible hybrid