The Systems Software Group at FAU Erlangen-Nürnberg offers a funded doctoral scholarship. We are looking for a highly motivated and talented PhD candidate to join our research group working at the intersection of Trusted Execution Environments (TEEs) and Federated Machine Learning. This doctoral scholarship provides the recipient with the opportunity to conduct research in close collaboration with our team and actively contribute to ongoing research projects. The successful candidate will work on secure, privacy-preserving, and trusted machine learning systems, including exploring the use of TEEs such as Intel SGX, AMD SEV-SNP, or ARM CCA in distributed and federated learning environments.

Your responsibilities

• Conduct research on the design and implementation of secure federate ML using TEEs
• Prototype and evaluate systems in realistic adversarial settings
• Collaborate with PhD students and postdocs, and contribute to academic publications
• Participate in regular group meetings and project updates

Your profile

• Master’s degree (or short before completion) in Computer Science, Computer Engineering, or a related field
• Solid background in at least one of: systems security, trusted computing, or applied machine learning
• Programming skills in Python, C/C++, or Rust
• Familiarity with TEEs (e.g., Intel SGX, AMD SEV-SNP) and interest in system-level research
• Strong analytical thinking and motivation to pursue independent research
• Excellent communication skills in English

What we offer

• A doctoral scholarship awarded for an initial period of 18 months (with the possibility of extension to 36 months)
• A monthly scholarship rate of 1400 €; in addition further supplementary benefits can be applied for under certain conditions
• Mentorship and integration into a lively and welcoming research team
• Access to cutting-edge infrastructure and resources
• A clear path to completing your PhD with us

How to apply

Please send the following documents in a single PDF to i4scholarship@cs.fau.de:

• Short motivation letter (approx. 1 page)
• CV
• Transcript of records
• If available: links to samples of prior research (e.g., thesis, paper, project) and LoR

We particularly encourage applications from under-represented groups in STEM and aim to foster an inclusive research environment.

Contact

If you have any questions, please contact i4scholarship@cs.fau.de:. Also ruediger.kapitza@fau.de.