Master-Thesis "Event-Driven Data Abstraction for AI-Powered Fault Diagnosis Using LLM"
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. The goal of your thesis will be to develop and evaluate event-driven data abstractions that distill raw time-series into concise, meaningful tokens, enabling the LLM assistant to detect and explain faults in real time and empower even non-specialists to resolve errors safely and efficiently.
Master-Thesis "Event-Driven Data Abstraction for AI-Powered Fault Diagnosis Using LLM"
CENTER FOR VISION, AUTOMATION & CONTROL
* Dive into the Literature: Conduct a comprehensive literature review on time-series abstraction, tokenization strategies, and LLM-mediated diagnostic workflows, and refine the thesis scope and crystallize clear research questions.
* Design and implement an Abstraction Library: Create a modular Python package implementing event-driven techniques (threshold alarms, change-point detection, windowed summaries, symbolic encoding, event clustering) that can be easily extended and integrated.
* Work with Diverse Data Sources: Process and integrate public benchmark datasets, ROS 2/Gazebo-simulated streams, and real industrial time-series to build realistic evaluation scenarios.
* Design & Execute an Evaluation Framework: Define and implement metrics (compression ratio, reconstruction fidelity, LLM diagnostic accuracy), craft effective prompt templates, integrate an open-source LLM, and perform systematic comparative experiments.
* Learn Scientific & Engineering Practices: Gain practical experience in scientific methodology, experimental design, data analysis workflows, version control, and code documentation. Participate in regular progress reviews and present interim findings to a panel of interdisciplinary experts.
* Prototype & Document Results: Collaborate with our researchers to refine your prototypes, interpret results, and generate actionable recommendations for best-practice abstraction strategies. Deliver production-quality code, reproducible experiments, and a polished thesis report.
Your qualifications as an Ingenious Partner:
* Ongoing master’s studies in Informatics, Software Engineering, Telematics, Applied Mathematics, or a related STEM field
* Strong programming skills, especially in Python and data-science libraries (NumPy, pandas), plus basic signal-processing/time-series analysis experience
* Enjoyment of application-oriented challenges in industrial contexts, translating theory into real-world solutions
* High level of commitment and team spirit, with the ability to collaborate effectively in multidisciplinary settings
* Excellent English skills, both written and spoken, for scientific communication and documentation
What to expect:
* Duration: 6-12 months (flexible start, ideally as soon as possible)
* Academic supervision support: If you already have a fitting supervisor at your university, that’s ideal; otherwise, we’ll help you connect with an appropriate advisor
* Intensive, hands-on mentorship, structured learning sessions, and regular interdisciplinary feedback from our experts, to ensure your professional growth and project success.
* Access to high-performance compute infrastructure
* Opportunity to publish results in peer-reviewed journals, present them at conferences and collaborate with leading industry partners
* Chance to tackle one of today’s most exciting AI challenges and to make a direct impact on industrial uptime, and launch your career in AI and data science
* EUR 1.005,06 gross per month for 20 hours/week based on the collective agreement. There will be additional company benefits. As a research institution, we are familiar with the supervision and execution of master theses, and we are looking forward to supporting you accordingly!
At AIT diversity and inclusion are of great importance. This is why we strive to inspire women to join our teams in the field of technology. We welcome applications from women, who will be given preference in case of equal qualifications after taking into account all relevant facts and circumstances of all applications.
Please submit your application documents including your CV, cover letter, relevantcertificates (transcript of records) online.
For further information please contact:
DE EN
DE EN
Follow us on:
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. The goal of your thesis will be to develop and evaluate event-driven data abstractions that distill raw time-series into concise, meaningful tokens, enabling the LLM assistant to detect and explain faults in real time and empower even non-specialists to resolve errors safely and efficiently.
Master-Thesis "Event-Driven Data Abstraction for AI-Powered Fault Diagnosis Using LLM"
CENTER FOR VISION, AUTOMATION & CONTROL
* Dive into the Literature: Conduct a comprehensive literature review on time-series abstraction, tokenization strategies, and LLM-mediated diagnostic workflows, and refine the thesis scope and crystallize clear research questions.
* Design and implement an Abstraction Library: Create a modular Python package implementing event-driven techniques (threshold alarms, change-point detection, windowed summaries, symbolic encoding, event clustering) that can be easily extended and integrated.
* Work with Diverse Data Sources: Process and integrate public benchmark datasets, ROS 2/Gazebo-simulated streams, and real industrial time-series to build realistic evaluation scenarios.
* Design & Execute an Evaluation Framework: Define and implement metrics (compression ratio, reconstruction fidelity, LLM diagnostic accuracy), craft effective prompt templates, integrate an open-source LLM, and perform systematic comparative experiments.
* Learn Scientific & Engineering Practices: Gain practical experience in scientific methodology, experimental design, data analysis workflows, version control, and code documentation. Participate in regular progress reviews and present interim findings to a panel of interdisciplinary experts.
* Prototype & Document Results: Collaborate with our researchers to refine your prototypes, interpret results, and generate actionable recommendations for best-practice abstraction strategies. Deliver production-quality code, reproducible experiments, and a polished thesis report.
Your qualifications as an Ingenious Partner:
* Ongoing master’s studies in Informatics, Software Engineering, Telematics, Applied Mathematics, or a related STEM field
* Strong programming skills, especially in Python and data-science libraries (NumPy, pandas), plus basic signal-processing/time-series analysis experience
* Enjoyment of application-oriented challenges in industrial contexts, translating theory into real-world solutions
* High level of commitment and team spirit, with the ability to collaborate effectively in multidisciplinary settings
* Excellent English skills, both written and spoken, for scientific communication and documentation
What to expect:
* Duration: 6-12 months (flexible start, ideally as soon as possible)
* Location: Vienna (hybrid)
* Academic supervision support: If you already have a fitting supervisor at your university, that’s ideal; otherwise, we’ll help you connect with an appropriate advisor
* Intensive, hands-on mentorship, structured learning sessions, and regular interdisciplinary feedback from our experts, to ensure your professional growth and project success.
* Access to high-performance compute infrastructure
* Opportunity to publish results in peer-reviewed journals, present them at conferences and collaborate with leading industry partners
* Chance to tackle one of today’s most exciting AI challenges and to make a direct impact on industrial uptime, and launch your career in AI and data science
* EUR 1.005,06 gross per month for 20 hours/week based on the collective agreement. There will be additional company benefits. As a research institution, we are familiar with the supervision and execution of master theses, and we are looking forward to supporting you accordingly!
At AIT diversity and inclusion are of great importance. This is why we strive to inspire women to join our teams in the field of technology. We welcome applications from women, who will be given preference in case of equal qualifications after taking into account all relevant facts and circumstances of all applications.
Please submit your application documents including your CV, cover letter, relevantcertificates (transcript of records) online.
For further information please contact:
Marco Wallner, marco.wallner@ait.ac.at
Apply now!
Tomorrow Today - with You?
Apply online now Back to job listing Print
Interested?
Drag your CV here or upload it to create a new profile.
;
AIT Austrian Institute of Technology GmbH
Giefinggasse 4
1210 Vienna
Austria
jobline@ait.ac.at
+43 50550-0
Imprint
Follow us
2025 AIT Austrian Institute of Technology
Anrufen
E-Mail
Locations and Subsidiary Enterprises
In your application, please refer to myScience.at and referenceJobID46757.
#J-18808-Ljbffr