This project aims to provide data-driven (evidence-based) solutions at both the macro and organizational levels to reduce high job turnover rates among Information Technology (IT) professionals. Europe faces a shortage of IT professionals, according to the European Union Digital Decade Agenda. Despite targeting 20 million IT professionals by 2030, the actual realization is only around half of the target. Additionally, at the organizational level, when an IT employee departs, the financial impact is up to 6 times higher than for other professionals. In these circumstances, satisfying current employees and absorbing additional IT specialists are essential to meet the needs of industrial ecosystems.

This fellowship aims to identify the critical determinants of job mobility among IT professionals in the EU. This study is interdisciplinary research spanning economics, statistics, and AI. As a research project in big data analytics, the project uses a hybrid approach that comprehensively assesses environmental, organizational, and individual factors within a multilevel model by combining panel data econometrics and machine learning techniques. Considering these factors, the results recommend public-sector policies and organizational strategies to retain these valuable employees. By implementing these recommendations, policymakers and organizations can create a more attractive IT work environment, ultimately reducing turnover in companies, especially big IT companies. In addition, governments would be able to create a more sustainable environment for digital transformation at the macroeconomic level.

Leidiane’s doctoral research project aims to facilitate trustworthiness in agricultural data sharing by addressing technical, legal and social dynamics.

This project is part of the EnTrust Doctoral Network. EnTrust is an intersectoral, international and interdisciplinary network with the aim to establish a new generation of Data Executives that are able to advance the state of the art in sharing data in a fair, transparent, and trusted environment. 

The research focuses on using Heritage Building Information Modeling (HBIM) as a key tool to document and analyze heritage buildings. While Building Information Modeling (BIM) offers many advantages, it remains challenging to accurately model the complex architectural forms found in historic structures. In the 2024 doctoral research of the project researcher, Sara Ben Lashihar, an automated methodology using a Revit add-in called BIMLash was developed. This tool converts point cloud data into a mesh directly within the BIM environment, without relying on external software, improving the efficiency and accuracy of modeling heritage masonry structures. Following the successful development of this tool, this postdoctoral research proposes the improvement of the output of this tool not only as solid geometry, parametric components but also to a tool that supporting the sustainable heritage conservation, that is aligned with Sustainable Development Goal SDG 11: Making cities and human settlements inclusive, safe, resilient, and sustainable.

This project is part of the DIGI+ postdoctoral research network.

This project aims to develop a standardised cyber risk assessment method tailored specifically to farmers operating within distributed data-sharing environments. The research focuses on designing security solutions that enable secure data access, mitigate cyber risks, and ensure authenticity and integrity of data provenance, including ownership and data sovereignty. As part of DR4, the project maps relevant cybersecurity frameworks and maturity models applicable to agri-data ecosystems. It also simulates potential cyber risk scenarios using open datasets and case studies to evaluate vulnerabilities and propose practical, scalable mitigation strategies. The overall objective is to strengthen trust, resilience, and secure collaboration in agricultural data infrastructures.

Outputs and Deliverables include mapping and comparative analysis of relevant cybersecurity frameworks and maturity models; simulation-based cyber risk scenarios using open datasets; and case study evaluations.

This project is part of the EnTrust doctoral network funded by the Marie Skłodowska-Curie Actions (MSCA) under Horizon Europe. MSCA is the European Union’s flagship funding programme for doctoral education and postdoctoral training of researchers.

This research addresses the critical lack of data sovereignty in agricultural data sharing ecosystems, and aims to develop technical and governance frameworks that enable farmers and agricultural stakeholders to maintain control over their data through usage control mechanisms and digital policy enforcement.

This project is part of the EnTrust Doctoral Network. EnTrust is an intersectoral, international and interdisciplinary network with the aim to establish a new generation of Data Executives that are able to advance the state of the art in sharing data in a fair, transparent, and trusted environment.