Master Thesis: Robust AI inference within a WebAssembly Engine (Waldkirch (near Freiburg), Waldkirch (near Freiburg), Waldkirch (near Freiburg), Waldkirch ) @ SICK

<p><strong>Winter Semester 2026/27 - limited to 5-6 month</strong></p> <p> </p> <p><strong>YOUR TASKS</strong></p> <ul> <li>Familiarization with the basics of WebAssembly (Wasm), bytecode structures, and AI inference workloads</li> <li>Analysis of WebAssembly bytecode in the context of AI inference</li> <li>Investigation of the data flow of AI inference bytecode during execution in a selected Wasm engine</li> <li>Identifying potential for expansion in the Wasm engine to increase robustness, fail-safety, and reliability</li> <li>Designing and prototyping approaches for data flow monitoring of AI inference within a WebAssembly engine.<br> </li> </ul> <p><strong>YOUR PROFILE</strong></p> <ul> <li>Master&apos;s degree in computer science or a comparable field of study</li> <li>Good knowledge of C++ and/or Rust</li> <li>Good knowledge of machine learning models and their architectures</li> <li>Ideally, initial experience in compiler construction, software engineering principles, and/or system development</li> <li>Interest in compiler construction, software engineering, and system software in the context of AI inference</li> </ul><p><strong>Contact:</strong> Sarah Disch</p>