DC5 – Unchange

DC5: Non-invasive macro scale identification and distribution of chemical degradation in paintings

Doctoral candidate

To be recruited

Hiring institution

National research council of Italy (CNR)

PhD enrolment institution

University of Perugia (UNIPG)

Main supervisor

Francesca Rosi (CNR)

Co-supervisor

Alamin Mansouri (UBE)

Co-supervisor

Francesco Paolo Romano (CNR)

Develop innovative non-invasive monitoring strategies for the early detection and mapping of chemical degradation phenomena in paintings before they become visible to the naked eye, by exploiting a broad spectral range from X-rays to the mid-infrared through multimodal hyperspectral imaging.
To integrate analytical, morphological, and optical information into unified multimodal datacubes, enabling correlative interpretation of chemical and structural degradation.
To design and implement AI-informed algorithms for data fusion, feature extraction, and anomaly detection across multimodal datasets.
To develop interactive digital visualization tools for enhanced representation and exploration of degradation patterns and material changes in the artworks.

An optimized multimodal analytical workflow fulfilling the requirements of non-invasiveness, portability, specificity, and sensitivity for the identification and mapping of common degradation materials.
AI-assisted data-processing tools capable of analyzing complex multimodal datacubes, extracting chemically and morphologically relevant information, and identifying early-stage degradation markers.
A digital platform for interactive visualization of integrated multimodal data, supporting advanced correlation, and virtual inspection of degradation features at the macro-scale.