University of Cambridge
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5 days ago
Cambridge – Tübingen Ph.D. Fellowship
Cambridge–Tübingen Ph.D. Fellowship in Machine Learning University of Cambridge in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Ph.D. fellowship program; funding details are not specified in the post.
Country
United Kingdom
University
University of Cambridge

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About this position
Cambridge–Tübingen Ph.D. Fellowship Call 2027 is open for candidates interested in machine learning, empirical inference, statistics, and related areas. The fellowship connects the University of Cambridge Machine Learning Group with the Max Planck Institute for Intelligent Systems in Tübingen, offering a joint research environment across two leading centres.
The post highlights principal supervisors at Cambridge: Carl Edward Rasmussen, Jose Miguel Hernandez Lobato, Adrian Weller, Hong Ge, and Miri Zilka, and in Tübingen: Bernhard Schölkopf. The opportunity is aimed at applicants whose research interests are well matched to these supervisors and their groups.
This is a Ph.D. fellowship announcement rather than a postdoc or master’s opening. The post does not specify stipend, tuition coverage, or other funding details, but it clearly refers to a fellowship call. No explicit deadline is stated in the post text.
To apply, follow the linked call page, review the fellowship information, and prepare an application aligned with the listed supervisors and research themes. The application link is provided in the post.
Funding details
Ph.D. fellowship program; funding details are not specified in the post.
What's required
Applicants should have research interests well matched to the principal supervisors in Cambridge and Tübingen. The call is for the next generation of machine learners and is specific to candidates whose interests align with the listed supervisors and research groups.
How to apply
Use the application link provided in the post to open the Cambridge–Tübingen Ph.D. Fellowship Call 2027 page. Review the call details and submit an application through the linked portal. Match your research interests to the listed supervisors before applying.
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