Master Thesis: Automated Analysis of System Failures Using LLMs
vor 2 Wochen
We present an exciting research opportunity:
Thesis: LLM-Driven Root-Cause Analysis for Unexpected System BehaviorYour ResponsibilitiesWe are in search of a dedicated student to undertake a master thesis focused on LLM-based automated root-cause analysis tailored for the automotive sector. Your role will involve developing methodologies to scrutinize extensive textual information sourced from various platforms. Collaborating with industry specialists, you will engage in prompt engineering and fine-tuning of large language models. The aim of this thesis is to understand the relationships between observations in time-series data and their correlations with the physical systems, ultimately ranking potential failure modes and clustering similar failure conditions. This research will provide significant insights into identifying root causes of powertrain system issues.
- Compile data from diverse sources (e.g., FP-sheets, technical documents, presentations, word files) to establish a comprehensive knowledge database.
- Design the database architecture for effective processing of textual information.
- Implement basic analytics to identify deviations from expected behaviors or utilize existing analytical results. Assign suitable textual descriptions to observations (e.g., elevated battery cell temperature, reduced pack voltage, normal contactor temperature, etc.).
- Employ a variety of techniques (e.g., few-shot classification, RAGs, Chain-Of-Thoughts) from the realm of LLMs to discern the relationships between observations and the content of the knowledge database.
- Rank the most probable failure modes along with their likelihoods for specific combinations of observed conditions.
- Group similar observed conditions associated with different failure modes within the knowledge database.
- Establish a validation strategy utilizing appropriate metrics from existing literature to assess the outcomes of the analysis.
- Bachelor's degree in relevant fields such as Applied Statistics/Mathematics, Computer Science, Data Science, Automotive Engineering, or Electrical Engineering.
- Practical experience in data analysis, machine learning, natural language processing, and database management.
- Familiarity or experience with open-source LLMs, as well as public APIs and libraries (e.g., Hugging Face) for LLM applications.
- Strong command of Python and application of data analysis techniques and algorithms.
- Capability to work autonomously, conduct experiments, and interpret complex datasets.
- Excellent problem-solving and analytical thinking abilities.
- Effective communication skills to convey findings and recommendations clearly.
- The opportunity to conduct your thesis independently while receiving professional guidance and support from our experienced team.
- Engagement with experts in the company, allowing for the exchange of ideas and knowledge.
- An immersive experience in the AVL environment, bridging theoretical knowledge with practical application.
About AVL
AVL stands as a global leader in mobility technology, specializing in development, simulation, and testing within the automotive industry and beyond. The company delivers innovative concepts, solutions, and methodologies across various domains, including vehicle development, e-mobility, automated and connected mobility (ADAS/AD), and software aimed at fostering a greener, safer, and better mobility landscape.
The successful completion of the thesis will be compensated with a one-time fee of EUR 3,500 before tax.
If you are eager to explore the future of mobility and contribute to meaningful advancements, we encourage you to consider this opportunity.
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Graz, Steiermark, Österreich AVL List GmbH VollzeitWe present an exciting research opportunity:Thesis: LLM-Based Automated Root-Cause Analysis for Unexpected System BehaviorYour ResponsibilitiesWe are in search of a dedicated student to undertake a master's thesis focused on LLM-based automated root-cause analysis within the automotive sector. The candidate will be tasked with developing methodologies to...
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Graz, Steiermark, Österreich AVL List GmbH VollzeitWe present an exciting research opportunity:Thesis: LLM-based Automated Root-Cause Analysis for Unexpected System BehaviorYour ResponsibilitiesWe are in search of a dedicated student to undertake a master's thesis focused on LLM-based Automated Root-Cause Analysis within the automotive sector. Your role will involve the development of methodologies to...
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Graz, Steiermark, Österreich AVL List GmbH VollzeitWe present an exciting research opportunity:Thesis: LLM-Based Automated Root-Cause Analysis for Unexpected System BehaviorYour ResponsibilitiesWe are looking for a dedicated student to undertake a master's thesis focused on LLM-based automated root-cause analysis within the automotive sector. Your role will involve developing methodologies to assess...
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