Time Series Representation Learning for Industrial Process Characterization

KU Leuven; Belgique; On-site; EUR 40,000 - 50,000; Full time

Benefits offered by this job

PhD stipend

State-of-the-art facilities

Job summary

KU Leuven Bruges Campus in West Flanders seeks a highly motivated PhD candidate in Computer Science/AI to join a dynamic robotics and data analytics team. The position offers a fully funded 4-year PhD scholarship with a competitive remuneration package and access to state-of-the-art lab facilities, supporting discovery and industrial collaboration with partners.

You will develop explainable representations from high-dimensional industrial time series, design motif mining algorithms, and publish

Qualifications

• Master's degree in Computer Science, Artificial Intelligence, Electrical Engineering, Mathematical Engineering or related field.
• Proficient in written and spoken English.
• Experience with machine/deep learning; time series analysis is a plus.
• Proficient in Python and familiar with data science toolkits.

Responsibilities

• Develop explainable representations from high-dimensional industrial time series data.
• Design motif mining algorithms for multivariate time series and learn relationships among motifs.
• Balance scalability and detail in motif-based representations; ensure practical applicability in industry contexts.
• Collaborate with industrial partner(s) and publish research outcomes in conferences and journals.

Skills

English proficiency

Machine learning

Python

Time series analysis

Independent planning

Education

Master's degree in Computer Science or related field

Tools

Python

Data science toolkits

Job description

Organisation/Company KU LEUVEN Research Field Computer science » Informatics Technology » Information technology Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Application Deadline 30 Nov 2026 - 23:59 (UTC) Country Belgium Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Feb 2027 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number BAP-2026-637 Is the Job related to staff position within a Research Infrastructure? No

Offer Description

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

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 wellabove 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.

The position will be hosted within the collaborative and internationally oriented research environment at KU Leuven, one of the world'sleading universities (ranked among the top 100 globally). Founded in 1425, KU Leuven has been a center of learning for nearly six centuriesand is Belgium’s highest-ranked university, as well as one of the oldest and most renowned universities in Europe. KU Leuven provides atruly international experience, high-quality education, world-class research, and cutting-edge innovation, having topped Reuters' ranking ofEurope'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 inBelgium, 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 researchgroup, 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 willvalorise 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 strongemphasis on publishing high-quality conference papers and journal articles. They will benefit from our robust international research andindustrial 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 closeproximity to a network of companies. The campus features newly established labs to support both educational and research needs.

Eligibility criteria

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 wellabove 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.

We offer:

• A fully funded 4-year PhD scholarship, with a remuneration package competitive with industry standards inBelgium, 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 researchgroup, 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 willvalorise 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 strongemphasis on publishing high-quality conference papers and journal articles. They will benefit from our robust international research andindustrial 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 closeproximity to a network of companies. The campus features newly established labs to support both educational and research needs.