The current technological revolution is largely driven by spectacular progress in artificial intelligence (AI). Yet, although the huge potential is widely recognized, the lack of reliability of AI technology is still considered a serious issue of concern, limiting its adoption both by industry and society at large. Indeed, aspects such as safety, security, and privacy-preservation are essential prerequisites for the use of AI in domains of public interest – e.g. ensuring that robots do not endanger life or respecting confidentiality of data.
The vision of the 'Konrad Zuse School of Excellence in Reliable AI' (relAI) is to train future generations of AI experts, who for the first time combine technical brilliance with awareness of the importance of AI’s reliability. Our novel, highly innovative AI program will educate top international candidates in the end-to-end development of reliable AI systems (including scientific knowledge, business expertise, and industrial exposure), both for industry and academia, and perform cutting-edge research to make AI ready for deployment in critical application domains.
Table of Content
Summary
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Benefits
The students and doctoral researchers build the heart of relAI.
- To ensure that the selected MSc students can fully concentrate on the offered training programs and their initial research activities, they receive a monthly scholarship of 934€, plus additional allowances for partners and children, for two years.
- Doctoral researchers are employed on 100% TV-L E13 for three years, ensuring a competitive salary in a highly contested job market.
Besides the funded positions, it is also possible to become an associated relAI PhD student. If your field of research fits well with the topic of relAI, one of your supervisors is already a relAI Fellow, and you have third-party funding, you can become an associated PhD. Associated PhDs complete the same curriculum and enjoy the same benefits.
Requirements
- Excellent bachelor's degree / master's degree (for application for MSc / PhD respectively) in computer science, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines
- a genuine interest to work on a topic of reliableAI, covering aspects such as safety, security, privacy and responsibility in one of relAI’s research areas Mathematical & Algorithmic foundations, Learning & Instruction, Algorithmic Decision-Making, Medicine & Healthcare or Robotics & Interacting Systems
Please note:
- relAI MSc students have to be inscribed in an MSc program in computer science, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines at TUM or LMU, so make sure that you apply to that in parallel to the application to relAI. Most master application deadlines are end of May. Offers will be made subject to acceptance into such a program. So no need to worry if you are not accepted before submitting your relAI application.
- The funding is limited to the remaining duration of the recipient’s studies according to the model study plan (“Regelstudienzeit”). Shorter scholarship durations can be determined by the Selection Committee based on available budget and the specific study situation and progress of the candidate. If the prerequisites for receiving a scholarship are no longer satisfied, then the scholarship will end immediately.
Application Documents
You need the following documents/information for starting the application process:
- Your CV
- Copies of your relevant diplomas and transcripts (in case a degree is still ongoing you can upload transcripts to prove you will have finished by the time you join relAI)
- Short Motivation (max 1500 characters), also highlighting previous experience in the field,
- PhD program only:
- Choice of two preferred supervisors who are relAI Fellows, along with a short explanation (max 1000 characters) for each why the respective supervisor would be a good fit (illustrate with a thesis topic you would like to pursue if you have one in mind)
- Names and addresses of two researchers who consent to be contacted for recommendation letters
- Proof of English language proficiency: level C1 or equivalent
(also acceptable: proof that University degree was taught mainly in English)
Check also:
2026 Fully Funded Humboldt Research Fellowship
2026 Eutopia PhD Co-tutelle Call
Application Deadline
July 15, 2026How To Apply
Interested and qualified? Go to Konrad Zuse School of Excellence in Reliable AI on www.portal.graduatecenter.lmu.de to apply
For more information, kindly visit Konrad Zuse School scholarship webpage.
