PhD in Machine Learning in Medicine Faculty of Medicine 100% Bern Temporary

PhD in Machine Learning in Medicine

UniBE is ambitious.
With us, you deliver top performance and test your skills against the best.

About the project

Chronic diseases often develop gradually over years, and their trajectories vary widely between individuals. Understanding this progression is key to early detection and timely intervention. Large multimodal health datasets combining medical imaging, wearable sensor data and clinical measurements now make it possible to study disease progression from multiple perspectives.

The goal of this project is to develop machine learning methods that learn from multimodal health data to characterise and model disease progression. Methodological focus areas the candidate may explore include:

• Multimodal representation learning across imaging, time series and tabular data
• Disease progression modelling from cross-sectional and sparse longitudinal data
• Causal machine learning to disentangle disease progression from treatment effects
• Learning with missing modalities and irregularly sampled signals
• Fairness and bias in multimodal models across demographic subgroups

The candidate

We are looking for a curious and motivated PhD candidate with a keen interest in doing machine learning research in the healthcare space. You should hold a Master's degree in a quantitative discipline such as computer science, machine learning, statistics, biomedical or electrical engineering, or similar. You have solid knowledge of the foundations of machine learning, are willing to dig deep to understand a problem, and have strong analytical skills. You have practical experience implementing and evaluating machine learning models in Python. Experience processing biomedical data (imaging, wearable, tabular) is a plus. Strong research infrastructure and international network

Application and Contact

Applications received by 30 November 2026 will receive full consideration. After the deadline, we may continue to review applications until the position is filled.

Applications should include your CV, full transcripts (BSc and MSc), and a cover letter (max 1 page) telling us about your motivation and something you are particularly proud of or passionate about.

PhD in Machine Learning in Medicine

UniBE is ambitious.
With us, you deliver top performance and test your skills against the best. Start Date as soon as possible Employment Relationship 100%, 3 years and option to extend by 1 year Institution / Workplace Machine Learning in Medicine Lab, University of Bern, Switzerland Bern With us, you create value.
For yourself and for society.

About us

You will be supervised by Prof. Dr. Lisa Koch, head of the Machine Learning in Medicine Lab (www.mlm-lab.ch). Our research group develops trustworthy machine learning towards clinical use. Our methodological interests include uncertainty quantification, robustness and reliability, representation learning, probabilistic modelling, and causal machine learning, with applications in biomedical imaging, wearable and tabular data. We publish at leading machine learning and medical imaging venues, collaborate closely with clinicians at Inselspital, Bern University Hospital, and maintain close links to research groups across Switzerland and internationally.

We are a small and collaborative team and value regular interactions. Bern offers a strong and growing medical AI community and an excellent quality of life.

About the project

Chronic diseases often develop gradually over years, and their trajectories vary widely between individuals. Understanding this progression is key to early detection and timely intervention. Large multimodal health datasets combining medical imaging, wearable sensor data and clinical measurements now make it possible to study disease progression from multiple perspectives.

The goal of this project is to develop machine learning methods that learn from multimodal health data to characterise and model disease progression. Methodological focus areas the candidate may explore include:

• Multimodal representation learning across imaging, time series and tabular data
• Disease progression modelling from cross-sectional and sparse longitudinal data
• Causal machine learning to disentangle disease progression from treatment effects
• Learning with missing modalities and irregularly sampled signals
• Fairness and bias in multimodal models across demographic subgroups

The candidate

We are looking for a curious and motivated PhD candidate with a keen interest in doing machine learning research in the healthcare space. You should hold a Master's degree in a quantitative discipline such as computer science, machine learning, statistics, biomedical or electrical engineering, or similar. You have solid knowledge of the foundations of machine learning, are willing to dig deep to understand a problem, and have strong analytical skills. You have practical experience implementing and evaluating machine learning models in Python. Experience processing biomedical data (imaging, wearable, tabular) is a plus. More Information
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Application Process

Your Benefits

Strong research infrastructure and international network Collaborative environment and ambitious team Healthy corporate culture and good working atmosphere Individual support and guidance Other benefits

Your Benefits

• Strong research infrastructure and international network
• Collaborative environment and ambitious team
• Healthy corporate culture and good working atmosphere
• Individual support and guidance

Your Salary

Your annual gross salary for the year 2026 amounts to 50'042 Swiss francs for full-time employment, including the 13th month salary.

Working at the University of Bern

The University of Bern not only offers exciting tasks but also an environment that actively promotes development, diversity, and equal opportunities. Discover what makes us stand out as an employer and how you can grow with us. Learn more
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Application and Contact

Applications received by 30 November 2026 will receive full consideration. After the deadline, we may continue to review applications until the position is filled.

Applications should include your CV, full transcripts (BSc and MSc), and a cover letter (max 1 page) telling us about your motivation and something you are particularly proud of or passionate about.

Questions about the position?

For subject-specific questions and information about the position, please contact [email protected]

Questions about the application?

[email protected]

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