(ref. BAP-2026-290)
Laatst aangepast: 16/05/26
Context
Machine failures in complex industrial systems can lead to significant downtime and costly service interventions. When a failure occurs, identifying not just what failed but why it failed remains a slow, manual process heavily reliant on expert intuition. The challenge is compounded by intricate failure mechanisms, diverse operating conditions, and data that is fragmented across sensor streams, maintenance logs, and error records.
Traditional root cause analysis are either too subjective or too resource-intensive for routine use, and they frequently miss novel or complex failure patterns. Emerging causal AI techniques offer a promising alternative: by modelling directed causal relationships from data rather than relying on correlations alone, they can uncover why failures occur and support more targeted corrective actions. However, applying these methods to real industrial systems remains difficult due to data heterogeneity, limited failure examples, and the need to incorporate engineering knowledge alongside learned structure.
Project
The central research challenge of this PhD position is to automatically learn why failures occur from heterogeneous industrial data - multivariate time-series sensor data, maintenance logs and system knowledge included in engineering models and manuals - and to do so reliably under the practical constraints of limited failure examples and continuously evolving operating conditions. You will design hybrid causal discovery methods (with a focus on time series) that combine complementary algorithmic families, including constraint-based, Bayesian, and gradient-based approaches, to extract causal graphs that capture the true dependencies driving failures. These data-driven graphs will be integrated with causal structure derived from physics-based models through a merging strategy that reconciles the two sources into a unified representation. Rather than treating this as a one-off analysis, the causal graph will be kept current through online updating strategies that incorporate incoming operational data as new observations arrive. Once a reliable causal graph is in place, the focus shifts to inference: estimating the actual effect sizes of candidate root causes using machine learning-based techniques such as Double Machine Learning, Meta-Learning, and Causal Forests, which are capable of handling complex, nonlinear relationships.
This PhD position is part of the CausAICA project, a collaborative research project supported by Flanders Make, the strategic reserach center of the manufacturing industry. The project aims at developing a causal AI framework for automated root cause analysis of failures in complex industrial machinery. The project integrates causal discovery and inference, physics-informed modelling, and agentic AI to build a system that links sensor data, maintenance logs, and engineering knowledge into explainable failure diagnoses. This PhD is embedded in the methodological heart of the project, responsible for developing the core causal discovery and inference methods that underpin the framework.
The methods developed throughout the PhD will be validated on realistic research demonstrators, as well as real-world industrial use cases leveraging operational field data provided by the industrial partners in the project.
We are seeking a highly motivated, enthusiastic, passionate, and communicative researcher, with a proactive and creative attitude who is eager to explore innovative solutions. If you recognize yourself in the story below, then you have the profile that fits the project and the research group:
We encourage candidates from diverse backgrounds and experiences to apply, as we believe that different perspectives contribute to better research and innovation.
Application Instructions for the PhD vacancy
To apply for this position, please use the online application tool and ensure that you submit the following documents in a single PDF file:
The position will be hosted within the collaborative and internationally oriented research environment at KU Leuven, one of the world's leading universities (ranked among the top 100 globally). Founded in 1425, KU Leuven has been a center of learning for nearly six centuries and is Belgium’s highest-ranked university, as well as one of the oldest and most renowned universities in Europe. KU Leuven provides a truly international experience, high-quality education, world-class research, and cutting-edge innovation, having topped Reuters' ranking of Europe's most innovative universities for four consecutive years.
We offer:
As a PhD candidate, you will be based at KU Leuven’s Bruges Campus (https://www.kuleuven.be/english/bruges), as part of a dynamic and interdisciplinary team of AI researchers, with access to state-of-the-art lab facilities to experimentally validate your findings in close collaboration with industrial partners.
The successful candidate will be encouraged to present their research at international conferences and national events, with a strong emphasis on publishing high-quality conference papers and journal articles. They will benefit from our robust international research and industrial network, which is actively involved in this project.
KU Leuven Campus Bruges, located in the magnificent medieval city of Bruges in West Flanders, offers a vibrant academic setting in close proximity to a network of companies. The campus features newly established labs to support both educational and research needs.
DTAI Lab at the Department of Computer Science
M-Group at KU Leuven Bruges Campus
KU Leuven strives for an inclusive, respectful and socially safe environment. We embrace diversity among individuals and groups as an asset. Open dialogue and differences in perspective are essential for an ambitious research and educational environment. In our commitment to equal opportunity, we recognize the consequences of historical inequalities. We do not accept any form of discrimination based on, but not limited to, gender identity and expression, sexual orientation, age, ethnic or national background, skin colour, religious and philosophical diversity, neurodivergence, employment disability, health, or socioeconomic status. For questions about accessibility or support offered, we are happy to assist you at this email address.
Heb je een vraag over de online sollicitatieprocedure? Raadpleeg onze veelgestelde vragen of stuur een e-mail naar solliciteren@kuleuven.be
av_timer Tewerkstellingspercentage: Voltijds
location_city Locatie: Brugge
timer Solliciteren tot en met:
18/06/2026 23:59 CET
bookmarks Tags: Computerwetenschappen, Wiskunde, Elektrotechniek, Industriële Ingenieurswetenschappen, Ingenieurswetenschappen, Wetenschappen
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