Forschungszentrum Jülich
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Master Thesis in Hybrid ML + LLM-Based Fault Detection and Explainability for Smart Building IoT Systems Forschungszentrum Jülich GmbH in Germany
Degree Level
Master's
Field of study
Computer Science
Funding
Reasonable remuneration is offered for the thesis. The post is a master thesis position within a research environment; no stipend amount or tuition coverage is stated.
Country
Germany
University
Forschungszentrum Jülich
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About this position
Master Thesis Opportunity at Forschungszentrum Jülich GmbH, Institute of Climate and Energy Systems – Energy Systems Engineering (ICE-1), in the Living Lab Energy Campus (LLEC) project.
The thesis focuses on hybrid ML + LLM-based fault detection and explainability for smart building IoT systems. The research setting includes large office buildings equipped with multiple IoT devices per room, transmitting real-time data such as window/door opening states, room air quality, luminance, and occupancy-related signals to a cloud infrastructure. The work also connects to a user-facing energy dashboard and desk booking system, enabling research on occupant-building interactions and intelligent operation of heating and shading systems.
Typical tasks include installation and configuration of occupancy detection sensors (e.g. PIR sensors, PQI meters, infrared camera), designing experimental plans for testing occupancy estimation approaches, analyzing methods by setup effort, cost, data quality, and suitability for different use cases, and comparing combinations of approaches in terms of detection accuracy and reliability.
Eligibility highlights: ongoing master's studies in computer science, data science, engineering, machine learning, or a closely related field; strong Python skills; solid knowledge of machine learning and AI; knowledge of fault detection in sensor data; strong analytical ability, initiative, self-motivation, English proficiency, and teamwork/communication skills.
Funding: reasonable remuneration is offered for the thesis.
Location: Jülich, Germany.
Application: apply through the online application portal. The post states that applications are accepted until the position is filled.
Funding details
Reasonable remuneration is offered for the thesis. The post is a master thesis position within a research environment; no stipend amount or tuition coverage is stated.
What's required
Ongoing master's degree studies in computer science, data science, engineering, machine learning, or a closely related field; good knowledge of 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 English; excellent teamwork and communication skills.
How to apply
Apply via the online application portal linked in the job post. For questions, use the provided contact form or career portal links.
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