Explainable Time Series Representations for Industry

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

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