Time Series Representation Learning for Industrial Process Characterization

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