University of Bern
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PhD in Machine Learning in Medicine and Multimodal Disease Progression Modelling University of Bern in Switzerland
Degree Level
PhD
Field of study
Computer Science
Funding
PhD position in a research lab; funding details are not specified in the post.
Deadline
Nov 30, 2026
Country
Switzerland
University
University of Bern

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About this position
PhD opening at the University of Bern in the Machine Learning in Medicine Lab for research on modelling disease progression from multimodal health data.
The project sits at the intersection of computer science, medical science, statistics, and public health, with data sources including imaging, wearables, and clinical records. Research directions mentioned include multimodal representation learning, causal machine learning, learning with missing data, fairness, and uncertainty quantification.
The lab describes itself as small and collaborative, and the post emphasizes that the work should matter for patients. The announcement is from Lisa Koch, Assistant Professor at the University of Bern, who is sharing the opening from her lab.
This is a PhD position. The deadline is 30 November 2026. Applications are submitted via the provided link, and an FAQ link is also included for more information. The post particularly encourages applications from women and groups underrepresented in machine learning.
Funding details
PhD position in a research lab; funding details are not specified in the post.
What's required
Applicants should be interested in machine learning research applied to medicine and able to work with multimodal health data, including imaging, wearables, and clinical records. The post highlights research directions such as multimodal representation learning, causal ML, learning with missing data, fairness, and uncertainty quantification. No specific degree, GPA, or language requirements are stated in the post.
How to apply
Submit your application through the provided application link. Review the FAQ link for additional details before applying. Applications are encouraged from women and groups underrepresented in ML.
More information can be found here
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