Master Thesis: Robust AI inference within a WebAssembly Engine

Winter Semester 2026/27 - limited to 5-6 month

 

YOUR TASKS

• Familiarization with the basics of WebAssembly (Wasm), bytecode structures, and AI inference workloads
• Analysis of WebAssembly bytecode in the context of AI inference
• Investigation of the data flow of AI inference bytecode during execution in a selected Wasm engine
• Identifying potential for expansion in the Wasm engine to increase robustness, fail-safety, and reliability
• Designing and prototyping approaches for data flow monitoring of AI inference within a WebAssembly engine.
   

YOUR PROFILE

• Master's degree in computer science or a comparable field of study
• Good knowledge of C++ and/or Rust
• Good knowledge of machine learning models and their architectures
• Ideally, initial experience in compiler construction, software engineering principles, and/or system development
• Interest in compiler construction, software engineering, and system software in the context of AI inference