As Austria's largest research and technology organisation for applied research, we are dedicated to make substantial contributions to solving the major challenges of our time, climate change and digitalisation. To achieve our goals, we rely on our specific research, development and technology competencies, which are the basis of our commitment to excellence in all areas. With our open culture of innovation and our motivated, international teams, we are working to position AIT as Austria's leading research institution at the highest international level and to make a positive contribution to the economy and society.

Our Center for Vision, Automation & Control (VAC) is a team of approximately 140 experts carrying out applied research in image processing, sensor-data fusion, machine learning, data analytics, mathematical modeling for real-time applications, process automation, and control of complex dynamic systems for industrial use. Within VAC, the Competence Unit Assistive & Autonomous Systems (AAS) located in Vienna invites applications for a Master Thesis Project. Our Competence Unit researches and develops technology components for assistance systems and the automation of commercial vehicles and unmanned aerial platforms, focusing on environment-perception techniques (e.g., multi-spectral cameras, laser- and radar-based sensors, odometry, and satellite navigation) to support efficient, flexible, and safe AI-driven assistance. VAC works closely with national and international scientific and industry partners to advance prototypes from laboratory demonstrations through to industrial deployment.

Throughout your master’s thesis you will contribute to the TeleAssist-FFG-Project , an initiative to transform industrial teleoperation into a truly collaborative experience. By combining live sensor and machine‐state feeds with embedded technical documentation in an LLM‐driven backend, TeleAssist doesn’t just let operators pilot machines remotely - it monitors for anomalies, interprets error codes, and proactively guides users through troubleshooting steps as they work.

In this thesis, you’ll harness the same retrieval ‑ augmented techniques (RAG) that power apps like ChatGPT to turn manuals and incident logs into an intelligent, context ‑ aware support system. This is your opportunity to tackle one of today’s hottest AI topics, deliver immediate real‑world impact in industrial maintenance, and supercharge your career in AI and data science.

• Learn under expert guidance: our team will guide you on how to learn and immerse yourself in both the theoretical and practical aspects of RAG systems.
• Dive into the Literature: Conduct an in‑depth review of RAG architectures and open‑source frameworks for document ingestion, retrieval, and LLM orchestration.
• Document Ingestion & Preprocessing: Collect and preprocess a corpus of open‑source equipment manuals, synthetic intervention reports, and maintenance logs and prepare it for use with real-world industrial data.
• Chunking Strategy Implementation: Implement and compare multiple segmentation methods, e.g., fixed‑length, sentence/paragraph‑based, and recursive chunking.

• Vector Database and RAG Pipeline: Deploy and configure a suitable database and assemble an end-to-end RAG pipeline design prompt templates and retrieval-generation fusion patterns for troubleshooting dialogues.
• Benchmark Datasets and Evaluation Framework: Setup and manage a representative benchmarking dataset and define suitable metrics for performance evaluation.
• Prototype & Document Results: Collaborate with our researchers to refine your prototypes, interpret results, and generate actionable recommendations for best-practice strategies. Deliver production-quality code, reproducible experiments, and a polished thesis report.