FZ Juelich Master Thesis - Hybrid ML + LLM-based Fault Detection and Explainability for Smart Building IoT Systems 2026

At the Institute of Climate and Energy Systems - Energy Systems Engineering (ICE-1), we develop advanced models and algorithms for the simulation and optimization of integrated multi-energy systems. Within the Living Lab Energy Campus (LLEC) project, large office buildings are equipped with many IoT devices, typically 5-10 devices per office room. These devices transmit real-time data such as the opening state of windows, doors as well as room air quality and luminance measurements to a cloud infrastructure. Additionally, through a user-facing energy dashboard with a desk booking system, building occupants can interact bidirectionally with the building systems. These occupant-building interactions are the basis for several avenues of research including the intelligent operation of heating and shading systems. As part of our team, you will contribute to the smooth and reliable operation of the building systems through implementation of online fault detection and diagnosis, and subsequent communication of the results to real users in a real-world setting.

Your Job

• Installation and configuration of available occupancy detection sensors (e.g. PIR sensors, PQI meters, infrared camera, etc.)
• Setup of suitable experimental plan for testing the occupancy estimation approaches
• Analysis of characteristics of the various approaches in terms of ease of setup, cost, data quality, suitability for different end-uses, etc.
• Analysis of combinations of approaches in terms of detection accuracy and reliability

• Table of Content
• About Forschungszentrum Julich
• Master Thesis - Hybrid ML + LLM-based Fault Detection and Explainability for Smart Building IoT Systems Master's Scholarship
• Aim and Benefits of Master Thesis - Hybrid ML + LLM-based Fault Detection and Explainability for Smart Building IoT Systems Master's Scholarship
• Requirements for Master Thesis - Hybrid ML + LLM-based Fault Detection and Explainability for Smart Building IoT Systems Master's Scholarship Qualification
• Application Deadline
• How to Apply

Master Thesis - Hybrid ML + LLM-based Fault Detection and Explainability for Smart Building IoT Systems Master's Scholarship

Aim and Benefits of Master Thesis - Hybrid ML + LLM-based Fault Detection and Explainability for Smart Building IoT Systems Master's Scholarship

• Meaningful Tasks: Your thesis deals with a future-oriented, socially relevant topic with direct practical relevance in an international environment
• Practical relevance: Hands-on as well as theoretical work in a Living Lab setup with existing tools and supporting frameworks
• Scientific environment: You can expect excellent scientific equipment, modern technologies, and qualified support from experienced colleagues
• Personal responsibility: You organize your tasks independently—from preparation to implementation
• Onboarding & teamwork: You can look forward to working in a dedicated, international, and collegial team. It is important to us that you quickly settle into the team and are given structured training for your tasks. FZ Juelich also support you from the very beginning and make your start easier with the Welcome Days and Welcome Guide
• Work-life balance: FZ juelich offer flexible working hours to help you balance your professional and personal life. You also have the option of flexible working (in terms of location), which is generally possible after consultation and in line with upcoming tasks and (on-site) appointments
• Flexibility: Flexible working hours make it easier for you to balance work and study
• Health & well-being: Your health is important to FZ Juelich. You can look forward to a comprehensive company health management programme with a wide range of options, including a beach volleyball court, running groups, yoga classes and much more. In addition, the company medical service and an experienced social counselling team are available to assist you on site
• Fair remuneration: FZ Juelich will pay you a reasonable remuneration for your thesis

Requirements for Master Thesis - Hybrid ML + LLM-based Fault Detection and Explainability for Smart Building IoT Systems Master's Scholarship Qualification

• Ongoing masters degree studies in computer science, data science, engineering, machine-learning, or other closely related field
• Good knowledge of programming in Python
• Good knowledge of machine-learning and artificial intelligence
• Knowledge of fault detection in sensor data
• Good analytical skills, initiative, and self motivation
• Good command of the English language
• Excellent teamwork and communication skills

Application Deadline

How to Apply

For more information, kindly visit FZ Juelich Master Thesis webpage.