Time Series Representation Learning for Industrial Process Characterization

The M-Group at KU Leuven Bruges Campus is an interdisciplinary research team focusing on intelligent and dependable mechatronic systems, combining research expertise from the departments of Computer Science, Electrical Engineering and Mechanical Engineering. One of the key research tracks focusses on the application of Artificial Intelligence and Machine Learning in real-world industrial settings on the one hand. The objective of the proposed PhD position is to investigate how novel techniques for time series representation learning on sensor data origination from complex and dynamic industrial processes can be leveraged for process characterization across multiple time series data mining tasks, such as anomaly detection, anomaly diagnosis and root cause analysis. The successful candidate will be offered to opportunity to pursue a PhD in Computer Science at KU Leuven, and will also be embedded within the Declarative Languages and Artificial Intelligence (DTAI) lab (https://dtai.cs.kuleuven.be), which pursues excellence in an explicit and synergistic combination of fundamental and applied research on machine learning and artificial intelligence. Doctoral training is provided in the framework of the Leuven Arenberg Doctoral School (https://set.kuleuven.be/phd). The PhD will be supervised by Prof. Mathias Verbeke and Mr. Louis Carpentier. Project Context Industry 4.0 has equipped industrial processes with large numbers of sensors, enabling continuous monitoring of machine and process behaviour. The resulting data allow the process to be modelled, which in turn yields analytical insights for optimising the process or for detecting and preventing faults. However, industrial processes are becoming increasingly complex, with many interacting components, varying operating conditions, and hundreds of monitored variables, which makes modelling the process as a whole increasingly difficult. Existing approaches rely mostly on deep neural networks to learn representations of the data, which are then leveraged in downstream tasks. While effective in terms of predictive performance, these models provide little understanding of the process itself, which is crucial in real-world applications. Post-hoc explanations do not solve this issue, as they only approximate the model's reasoning and are not guaranteed to faithfully reflect its behaviour. Furthermore, deep neural networks typically require large datasets to learn complex behaviours, which are rarely available in real industrial applications. Project The central research challenge of this PhD position is to automatically learn explainable representations from high-dimensional industrial time series data, and to leverage these representations across multiple time series data mining tasks. Within this PhD research, you will leverage motifs, i.e., representative subsequences, which act as the building blocks of the explainable time series representations and thereby offer a holistic view of the industrial process. To this end, you will design new algorithms for mining motifs in multivariate time series and for learning relationships among the discovered motifs. These algorithms will focus on three aspects. First, they will balance scalability and representativeness, i.e., the computational cost of building the motif-based representation against the level of detail and accuracy with which the motifs describe the process. Second, they will incorporate weak supervision, so that domain experts can steer the algorithms, and hence the motif-based representation, without needing to provide exhaustive labels. Finally, they will be simple to use, requiring minimal configuration with few hyperparameters whose meaning is intuitive to domain experts. The resulting representations will be evaluated in downstream tasks such as anomaly detection, anomaly diagnosis, and root cause analysis. The PhD position is part of an industrial research project with the aim of acquiring foundational knowledge about complex industrial systems and generating analytical insights that can be used to improve the production process. This PhD is embedded in the methodological core of the project, and is responsible for developing the representation learning methods on which the downstream analyses are built. The methods developed throughout the PhD will be validated on real-world industrial use cases leveraging operational data provided by the industrial partner in the project. Profile Profile We Are Seeking a Highly Motivated, Enthusiastic, 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
• I have a Master's degree in Computer Science, Artificial Intelligence, Electrical Engineering, Mathematical Engineering or a related field and performed well above average in comparison to my peers.
• I am proficient in written and spoken English.
• During my courses or prior professional activities, I have gathered experience with machine/deep learning, and can demonstrate a strong affinity with these fields. Prior experience with time series analysis is a plus.
• I am proficient in Python and am familiar with data science and machine/deep learning toolkits.
• As a PhD researcher at KU Leuven, I perform research in a structured and scientifically sound manner. I read technical papers, understand the nuances between different theories and implement and improve methodologies myself.
• Based on interactions and discussions with my supervisors and the colleagues in my team, I set up and update a plan of approach for the upcoming 1 to 3 months to work towards my research goals. I work with a sufficient degree of independence to follow my plan and achieve the goals. I indicate timely when deviations of the plan are required, if goals cannot be met or if I want to discuss intermediate results or issues.
• In frequent reporting, varying between weekly to monthly, I show the results that I have obtained and I give a well-founded interpretation of those results. I iterate on my work and my approach based on the feedback of my supervisors which steer the direction of my research.
• I feel comfortable to work as a team member and I am eager to share my results to inspire and being inspired by my colleagues.
• I value being part of a research group which is well connected to the mechatronics industry and I am eager to learn how academic research can be linked to industrial innovation roadmaps.
• During my PhD I want to grow towards representing the research group on project meetings or conferences. I see these events as an occasion to disseminate my work to an audience of international experts and research colleagues, and to learn about the larger context of my research and the research project. 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
• Motivation Letter: A letter (maximum 1 A4 page) addressing your strengths and qualifications in relation to the project.
• Complete Academic CV: A detailed CV including information about your education, current position, work experience (if any), employment gaps (if any), interests, extracurricular activities, international experiences, and projects demonstrating your programming/software skills, background knowledge relative to the project and level of expertise.
• List of Publications: If applicable, provide a list of your publications, including DOIs. Please do not include PDFs of the publications.
• Copies of Diplomas: Include copies of your BSc and MSc degrees.
• Transcript of Records: Provide transcripts for your BSc and MSc degrees. If you have not yet completed your Master's degree, include your available credits and scores, as well as a list of courses you are taking in the upcoming semester.
• English Summary of Master Thesis: A summary of your master thesis in English (maximum 1 A4 page, or 2 pages max when including a figure).
• Proof of English Language Proficiency: Documentation demonstrating your proficiency in English (TOEFL, IELTS, …), if available.
• Reference Letter or Contact Details: A reference letter or the contact information for one reference who can provide a recommendation letter upon request. Offer 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
• A fully funded 4-year PhD scholarship, with a remuneration package competitive with industry standards in Belgium, a country with a high quality of life and excellent health care system.
• Ample occasions to develop yourself in a scientific and/or an industrial direction. Besides opportunities offered by the research group, further doctoral training for PhD candidates is provided in the framework of the KU Leuven Arenberg Doctoral School (https://set.kuleuven.be/phd), known for its strong focus on both future scientists and scientifically trained professionals who will valorise their doctoral expertise and competences in a non-academic context.
• Opportunities to collaborate in groundbreaking research and participate in international conferences.
• Access to state-of-the-art infrastructure and a range of university benefits (health insurance, etc.).
• A dynamic, passionate team of fellow PhD students and test engineers. 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 Interested? For more information please contact Prof. Mathias Verbeke, mail: mathias.verbeke@kuleuven.be or Mr. Louis Carpentier, mail: louis.carpentier@kuleuven.be. You can apply for this job no later than November 30, 2026 via the online application tool 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.
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