Arficial intelligence assisted spectral unmixing for illuminated manuscripts
CRC - Centre de Recherche sur la Conservation
Laboratory
Hiring Policy
Starting date : before end of 2024
Duration : 1 year
Gross monthly salary : 2936 €
Application deadline : June 30, 2024
Subject
The goal of this project is to use machine learning (ML) algorithms to model how the reflectance
spectra of complex mixtures of colored materials from historical artworks, specifically illuminations
and/or decorated letters from ancient manuscripts, depend on their chemical composition. Covering
a wide range of energy from visible (VIS) to mid-infrared (mid-IR) coupled with X-ray fluorescence
spectroscopy (XRF), a spectral database of reference inorganic and organic materials will be built.
Subsequently, a supervised ML algorithm will be trained on the database, and the obtained ML
model will be capable of simultaneously predicting the composition of the colored layer and the
abundance of materials present in the mixture. Next, we will use the trained ML model to generate a
greater variety of artificial reflectance spectra, thus expanding the existing data set. A second ML
model will then be trained on the expanded data set to understand how the reflectance spectrum
correlates with the composition of the pictorial mixture. This second model will be applied to the
analysis of historical manuscripts. The candidate is expected to develop an approach that will offer
an efficient and general method for onsite determination of the composition of historical
illuminations based on their spectral response.
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Skills required
• Python programming
• Knowledge in machine learning algorithms (most specifically encoder-decoder NN and
transfer learning would be appreciated)
• Exper-se in reflectance and X-ray fluorescence imaging spectroscopies (optional)
• Experience with multispectral dataset analyses (optional)