PhD Researcher

Project background Fracture risk assessment today relies mainly on areal bone mineral density (DXA) which has limited discriminative power and lose accuracy over longer follow-up periods. Hip fractures carry the highest socioeconomic burden among osteoporotic fractures, yet they remain assessed largely from bone fragility alone, without properly accounting for fall biomechanics. Our group's simulation-based models capture how bone strength and fall mechanics combine to determine an individual's hip fracture risk, offering a more personalised picture than bone density alone. However, these simulations are computationally expensive, making them clinically impractical at scale. The solution lies in image-based, data-driven (AI) models that learn to predict personalised fracture risk directly from routine bone-density or CT scans in seconds. This PhD project will build and validate such AI models, extending them from single-timepoint prediction to longitudinal, personalised fracture-risk trajectories using repeat imaging. The successful candidate will therefore be working primarily as an AI/image-processing researcher, developing deep-learning models for medical images. Job description The PhD candidate will be embedded into a team of researchers developing AI models that produce markers of musculoskeletal health directly from medical images, as part of the Future Health Technologies 2 (FHT2) programme. Some key components in the work are: • Developing AI/deep-learning models, including generative and image-assessment approaches, that predict an individual's fracture risk directly from bone-density and CT images. • Extending these image-based models to longitudinal data, to forecast personalised bone-ageing trajectories and future fracture risk from repeat scans and clinical risk factors. • Validating model predictions against biomechanical simulations and real-world fracture outcomes across cohorts. • Co-supervision of MSc and BSc students. Selection Criteria Applicants are invited to apply for a PhD position at the Singapore-ETH Centre (SEC) in Singapore, available from 1 Dec 2026 (flexible start date). Funding is provided for up to four years. • Applicants should hold an MSc degree and have a strong background in computer science, engineering, mathematics, physics, or a related quantitative discipline. After successful completion of studies, the PhD degree will be awarded by ETH Zurich. • Candidates should be capable of working largely independently on complex research topics and demonstrate strong motivation. • We are particularly interested in applicants with a strong foundation in machine learning/deep learning and medical image processing, excellent Python programming skills, and prior experience training and evaluating neural network models (e.g. in PyTorch). • Familiarity with biomechanics or finite-element modelling is an advantage. • Background knowledge in parallel or high-performance computing is desirable, and applicants should be comfortable working on shared HPC clusters. • Experience with software engineering practices, including version control and collaborative development workflows, is required. • Applicants must be proficient in written and spoken English. Why SEC is your employer of choice? • Accredited with 5 Tripartite Standards by Tripartite Alliance for Fair & Progressive Employment Practices (TAFEP) Singapore. • A diverse workplace with 32 nationalities, offering ample opportunities for mutual learning. • Positive and inclusive working environment. • 25 days of annual leave for fixed-term contracts. • 1 day of Birthday Leave. • Annual dental benefits. • Committed to being a supportive employer as you prioritize your physical and mental wellness. • Comprehensive healthcare insurance coverage. • Flexible hybrid work arrangement (up to 2 days per week from home). • Abundant networking opportunities across various disciplines. • Accredited with NS mark certification. The Singapore-ETH Centre is an equal opportunity and family-friendly employer. All candidates will be evaluated on their merits and qualifications, without regards to gender, race, age or religion. Curious? So are we. We look forward to receiving your online application with the following documents: • A cover letter with a specific statement of your motivation for the project. • Your CV including the name and contact information of 2 references. • A copy of your university transcripts as PDFs. Applications via email or postal services will not be considered. Work location : 1 Create Way, CREATE Tower, Singapore 138602 (NUS University Town) Duration: Full-Time position (4-Year Contract) Further information about The Singapore-ETH Centre can be found on our website: https://sec.ethz.ch/ For further information, please contact: Prof. Dr. Benedikt Helgason (ETH Zurich) at bhelgason@ethz.ch, (strictly no applications)