The healthcare systems of the future must harness data effectively to support clinicians, allowing them to focus on patient care while leveraging AIto detect patterns beyond human perception, enhance diagnostic accuracy, optimise workflows, improve risk assessment and communication. Developing AI models that address these needs is particularly urgent in ageing societies, where rising patient numbers coincide with increasing workforce constraints.

A major contributor to disability in older adults is impaired musculoskeletal (MSK) health, with fall-related fractures representing a central research focus for our group. Despite their substantial socioeconomic burden, progress in prevention and screening has been limited, partly because fall and fracture risks are often addressed independently, despite their shared underlying mechanisms. A promising direction for AI in healthcare is the seamless integration of clinical image-based foundation models that can be fine-tuned across multiple use cases.

In this project, we aim to develop cross-modal AI models for MSK health evaluation by linking diverse clinical imaging datasets. The central innovation is the creation of a shared, tokenized latent space that enables data translation, risk modelling, and local deployment, even in settings with limited imaging data. Particular emphasis will be placed on integrating advanced biomechanical markers of bone health into AI workflows, supporting more precise and clinically actionable risk assessment.

The PhD candidate will be embedded into a team of researchers that is working on developing large medical image-based AI models for managing MSK health. 

Some key components in the work are: 

• Pretraining, adapting, and evaluating foundation models for medical image data using self-supervised and multimodal representation learning.
• Developing cross-modal image modelling methods, including cross-modal translation, multimodal fusion, and related generative or representation-learning approaches.
• Evaluating the learned models on clinically relevant downstream tasks, such as bone health assessment, anatomical and biomechanical biomarker estimation, and fracture-risk-related prediction.
• Co-supervision of MSc and BSc students.

Please note that the employment will be at the Singapore-ETH Centre in Singapore, and local working regulations will apply.

Funding is provided for up to four years. After successful completion of studies, the PhD degree will be awarded by ETH Zurich.

• Applicants should hold an MSc degree and have a strong background in computer science, engineering, mathematics, physics, or a related quantitative discipline.
• Candidates should be capable of working largely independently on complex research topics and demonstrate strong motivation. 
• We are particularly interested in applicants with a solid foundation in machine learning and AI, image processing, strong Python programming skills, and experience in software development. 
• Background knowledge in parallel computing or high-performance computing is desirable. 
• Experience with software engineering practices, including version control (e.g., git) and collaborative development workflows, is required. 
• Applicants must be proficient in written and spoken English.

• 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. 

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.

Further information about The Singapore-ETH Centre can be found on our website. Questions regarding the position should be directed to Dr Benedikt Helgason (ETH Zurich) bhelgason@ethz.ch.

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.