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

Forschungszentrum Jülich; Jülich; On-site; EUR 17,000 - 20,000; Part time

Benefits offered by this job

Flexible working hours

International team

Welcome Days and Welcome Guide

Job summary

Forschungszentrum Jülich is offering a thesis-oriented role in the Institute of Climate and Energy Systems – Energy Systems Engineering (ICE-1) in Jülich. You will install occupancy sensors, design experiments, and analyze estimation approaches for indoor environments within the Living Lab Energy Campus project.

Ideal candidates are master's students in CS/DS/Engineering with Python and ML knowledge, who can work in English, collaborate well, and contribute to fault detection in sensor data in a

Qualifications

• Ongoing master’s degree in CS/DS/engineering or ML.
• Strong Python programming knowledge.
• Experience with ML/AI concepts.
• Knowledge of fault detection in sensor data.
• Analytical mindset, initiative and motivation.
• Proficient in English and teamwork.

Responsibilities

• Installation and configuration of occupancy sensors (PIR, PQI meters, camera).
• Set up experimental plan for occupancy estimation testing.
• Analyze characteristics of approaches: setup cost, data quality, end-use suitability.
• Evaluate combinations of approaches for detection accuracy and reliability.

Skills

Python programming

Machine learning

Analytical skills

Teamwork & communication

English language

Education

Master's degree in computer science / data science / engineering / machine-learning or related field

Tools

Python ML libraries

Job description

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

Your Profile

• 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

Our Benefits for You

• 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. We also support you from the very beginning and make your start easier with our Welcome Days and Welcome Guide
• Work‑life balance: We 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 us. 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, our company medical service and an experienced social counselling team are available to assist you on site
• Fair remuneration: We will pay you a reasonable remunerationfor your thesis

In addition to exciting tasks and a collegial working environment, we offer you much more:

https://go.fzj.de/benefits

We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us.

The following links provide further information on diversity and equal opportunities:

https://go.fzj.de/equality

and on specific support options: https://go.fzj.de/womens-job-journey

Place of Employment: Jülich

Start Date: To the next possible date

Salary: We will pay you a appropriate remunerationfor your thesis

Application Deadline: The position will be published until it is successfully filled.