The Singapore-ETH Centre (SEC), established jointly by ETH Zurich and Singapore’s National Research Foundation (NRF), is inviting applications for a fully funded PhD Researcher position. Embedded within the Future Health Technologies (FHT) programme, this project focuses on advancing fracture risk assessment through data-driven deep learning models and medical imaging. Successful candidates will earn their doctoral degree from ETH Zurich while conducting cutting-edge research in Singapore.
Position Overview
| Position / Designation | PhD Researcher |
|---|---|
| Host Institution | Singapore-ETH Centre (SEC) |
| Degree-Awarding Body | ETH Zurich (Switzerland) |
| Programme | Future Health Technologies 2 (FHT2) |
| Research Field | AI, Deep Learning & Medical Image Processing |
| Location | Singapore (NUS University Town) |
| Duration & Work Type | Up to 4 years, Full-time (100%) |
| Expected Start Date | 1 December 2026 (Flexible) |
| Application Deadline | Open until filled (Early application encouraged) |
Research Area
The research is centered on Artificial Intelligence, Deep Learning, and Medical Image Analysis applied to musculoskeletal health and biomechanics. The project tackles hip fracture prediction by moving beyond conventional bone mineral density (DXA) scans to create generative and image-assessment AI models. Candidates will leverage routine CT and bone-density scans to produce personalized, longitudinal fracture-risk trajectories validated against biomechanical simulations.
Location
Singapore-ETH Centre
1 Create Way, CREATE Tower, Singapore 138602
(Located within National University of Singapore – NUS University Town)
Designation
PhD Researcher / Doctoral Candidate
Eligibility and Qualifications
Applicants must meet the following criteria to be considered:
- Educational Background: A completed Master’s (MSc) degree in Computer Science, Biomedical Engineering, Mathematics, Physics, or an equivalent quantitative discipline.
- Technical Expertise: Strong core competencies in machine learning/deep learning, medical image processing, and high proficiency in Python.
- Practical Experience: Prior hands-on experience training, evaluating, and deploying neural network architectures (e.g., PyTorch).
- Software Engineering Skills: Familiarity with version control (Git), collaborative code development, and running workloads on shared high-performance computing (HPC) clusters.
- Desirable Knowledge: Familiarity with biomechanics, finite-element modeling (FEM), or parallel computing is considered a clear advantage.
- Soft Skills & Languages: High degree of self-motivation, capacity for independent research, and excellent proficiency in written and spoken English.
Scholarship & Position Description
Hip fractures represent a substantial global socioeconomic burden. Existing diagnostic procedures rely heavily on bone fragility and ignore fall biomechanics. While complex biomechanical simulations yield deeper insight, they remain computationally prohibitive for real-time clinical workflows.
As a PhD Researcher, your core tasks will include:
- Designing and validating deep-learning models that forecast fracture risk directly from routine radiological images (CT and bone-density scans).
- Extending AI architectures to accommodate longitudinal data, predicting personalized bone-aging trajectories and long-term risk.
- Validating predictive AI outputs against biomechanical simulations and diverse real-world patient cohorts.
- Assisting with the co-supervision of Bachelor’s and Master’s thesis students.
What the Position Offers
- Full financial coverage/salary for up to four years under Singapore-ETH Centre terms.
- Doctoral degree conferred by the world-renowned ETH Zurich.
- 25 days of paid annual leave plus 1 day of Birthday Leave.
- Comprehensive medical and dental insurance coverage.
- Hybrid work flexibility (up to 2 days per week remote work).
- An international, multidisciplinary environment comprising over 30 nationalities at the CREATE campus.
How to Apply
Interested candidates must submit their application exclusively through the official ETH Zurich online recruitment portal. Applications sent via email or standard post will not be evaluated.
Prepare and upload the following documents in PDF format:
- A detailed Cover Letter highlighting your motivation, research interests, and suitability for the project.
- An up-to-date Curriculum Vitae (CV), including contact details of two academic references.
- Complete official University Transcripts (Bachelor’s and Master’s degrees).
Inquiries regarding the scientific aspects of the project can be addressed to Prof. Dr. Benedikt Helgason (ETH Zurich) at bhelgason@ethz.ch (do not send applications to this address).
Last Date for Apply
Applications are reviewed on a rolling basis until the position is filled. Candidates are strongly advised to apply as early as possible.
Apply Link
Apply Online via ETH Zurich Portal
