Description Fully homomorphic encryption (FHE)-based encrypted machine‑learning inference has attracted increasing attention. Conducting such inference on resource‑constrained edge devices introduces a distinct set of challenges. In this project you will first evaluate machine‑learning models that are amenable to efficient FHE deployment, and subsequently assess the feasibility of realizing the selected model using a verifiable FHE scheme. Responsibilities You are expected to familiarize yourself with a range of edge‑optimized machine‑learning models FHE schemes. You will critically assess the advantages and disadvantages of various models and FHE schemes, ultimately selecting a few promising combinations for further development. You will develop functional prototypes of machine‑learning inference using the chosen models and encryption schemes, and evaluate their performance on resource‑constrained edge devices. Consolidate your research in a scientific publication. Qualifications Qualifications Student enrolled in PhD Computer Science/Engineering. Familiarity with Fully Homomorphic Encryption techniques and frameworks Familiarity with Machine Learning techniques and frameworks Good programming skills in Rust, C, or C++ and in Python or Golang Language skills: English Location: Stuttgart (Germany)