Computer Science
[Closed]
Work description
- Study and extend methods of complex network-based time series analysis for high-dimensional and multivariate time series data. The work involves developing mapping methods that result in multi-layer networks, utilizing algorithmic approaches and data dimensionality reduction strategies. - Analyze the obtained complex networks using topological metrics of multi-layer networks and employ feature selection techniques to obtain the most representative metrics for a dataset. - Apply the methods to real-world multivariate time series datasets. - Extend the methodologies of time series analysis via complex networks to the context of spatiotemporal data. This work requires developing new methods for mapping spatiotemporal data into complex networks/spatial networks. - Apply the developed methods in the analysis of city-related data, particularly urban mobility, and urban air quality, among others. - Develop software with efficient implementations of the developed methods and make it available to the scientific community. - Publish the developed work in specialized journals.
Academic Qualifications
PhD in Computer Science
Minimum profile required
PhD Thesis must cover topics indicated in preference factos.
Preference factors
Evidence of scientific publications in the areas of complex networks and time series
Application Period
Since 26 Jun 2023 to 07 Jul 2023
[Closed]
Centre
Advanced Computing Systems