PhD Supervisor: David Camacho (UPM); Auxiliary supervisors: Francesco Piccialli (UNINA), Dariusz Mrozek (SUT), Volker Stolz (HVL); R&D cooperation: GMV

Objectives: The aim of this project will attempt to develop an integrated platform based on XAI and Visualisation techniques that combines the power of neural networks for feature extraction and representation learning with visual analytics for intuitive and interpretable insights into time series data. The end goal is to provide a comprehensive tool for domain experts be able to understand, interpret and potentially predict patterns and anomalies within time series data.

Expected Results: The main result of this project will be to develop a "Deep Visual Analytics" platform that leverages XAI techniques and deep learning models in order to detect anomalies and patterns in a series, which will be applied to various industrial domains, e.g., space or industry to demonstrate the feasibility of the implemented models and techniques. It will also be applied to various industrial domains, e.g., space or industry) to demonstrate the feasibility of the implemented models.

Applied research: The primary outcome of DC1 will be the innovation of XAI techniques and deep learning models, to effectively identify anomalies and patterns within time series data, which will be applied to diverse industrial domains, such as space and industry including applied research with GMV , and will serve to showcase the developed models and techniques.

Planned secondments: UNINA (4 months); SUT(4 months); HVL(4 months)                    

Enrolment in Doctoral degree: UPM

Recruitment of Doctoral Candidate: https://euraxess.ec.europa.eu/jobs/288653