Cambridge – Tübingen PhD Fellowships in Machine Learning

Cambridge – Tübingen PhD Fellowships in Machine Learning

The University of Cambridge Machine Learning Group and the Max Planck Institute for Intelligent Systems Empirical Inference Department in Tübingen are two of the world’s leading centres for machine learning research. In 2014, we launched a new and exciting initiative whereby a small group of select PhD candidates are jointly supervised at both institutions. The principal supervisors are Carl Rasmussen, Jose Miguel Hernandez-Lobato, Adrian Weller, Hong Ge and Miri Zilka at Cambridge University, and Bernhard Schölkopf in Tübingen. This program is specific for candidates whose research interests are well-matched to both the principal supervisors in Cambridge and Tuebingen.

This is a unique programme, and admission in the last years was highly competitive. We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific and social applications. There are no additional restrictions on the topic of the PhD but for further information on our current research areas please consult our webpages at http://mlg.eng.cam.ac.uk and https://is.mpg.de/ei/research.

The overall duration of the PhD will be four years, with roughly three years spent at one location, and one year spent at the other location, including initial coursework at the University of Cambridge. Successful PhDs will be officially granted by the University of Cambridge.

Applications to the Cambridge – Tübingen PhD Fellowships consist of three steps:

1. Apply to the ELLIS PhD Programme

Applicants should first apply to the ELLIS PhD Programme. Further information about the ELLIS PhD Programme and the application procedure can be found here: https://ellis.eu/news/ellis-phd-program-call-for-applications-2026. Please make sure to indicate supervisors from both the University of Cambridge and the Max Planck Institute for Intelligent Systems in Tübingen in your ELLIS PhD Programme application.

2. Apply to the Cambridge PhD Programme in Advanced Machine Learning

Applications should be also made by applying to the Cambridge PhD programme in Advanced Machine Learning as described here: https://mlg.eng.cam.ac.uk/phd_programme_in_advanced_machine_learning

Applicants should also make sure that they apply for “Cambridge Trusts”, “Gates Cambridge”, and “Other Research Councils” funding by ticking the relevant boxes in the application form.

3. Send your application documents to the Cambridge–Tübingen programme

Once you have completed the application to the Cambridge PhD Programme and the ELLIS PhD Programme, please send an email to camtue@is.mpg.de, indicating how your interests align with the programme and its available supervisors.

For the Cambridge application, you should download the application and send a copy to camtue@is.mpg.de. It is important to download the application before submitting it through the academic portal because the application is no longer available once it is submitted. Please use the subject line “Cambridge-Tuebingen PhD fellowship” in your email to camtue@is.mpg.de. Please arrange for a cv, a motivation letter, BSc. and MSc. transcripts, and two reference letters of your application to the Cambridge PhD programme in Advanced Machine Learning to be sent also to camtue@is.mpg.de.

Applicants from Europe don’t need an employment visa, for those who apply from non-EU countries it is necessary to inform themselves at the embassy of their country about visa procedures.

The hard deadline for formal applications is 31 October 2026, CET

Interviews will take place in December 2026.

The University of Cambridge is committed in its pursuit of academic excellence to equality of opportunity and to a proactive and inclusive approach to equality, which supports and encourages all under-represented groups, promotes an inclusive culture, and values diversity.

The Max-Planck Society is committed to increasing the number of individuals with disabilities in its workforce and therefore encourages applications from such qualified individuals. Furthermore, the Max Planck Society seeks to increase the number of women in those areas where they are underrepresented and therefore explicitly encourages women to apply.

We look forward to receiving your applications.

Carl Rasmussen - University of Cambridge

Bernhard Schölkopf - Max Planck Institute for Intelligent Systems