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Job Description

AI systems often have to cope with an overwhelming amount of input data. Time series often contain long sequences which have a similar behavior and which can be processed as a whole, rather than cutting it in a large amount of fixed-size epochs. In this PhD project, we will automate such segmentation processes as much as possible, with a focus on automatically splitting (multi-modal) time series into segments of variable sizes, within which the statistics are homogeneous across each segment. Such segmentation allows to model or process each segment with a different (more tailored) model, or to treat each segment as a higher-level object, which can be described or embedded as a single feature vector in further processing steps.

The main goal is to design a general-purpose segmentation pipeline and apply it in several use cases, for example in the analysis of electroencephalography (EEG) data for epilepsy.

Job Information

email redacted
Related URL
KU Leuven (University of Leuven)
Topic Categories
Leuven, Vlaams Brabant, Belgium
Closing Date
July 31, 2020