master Assignment
Heritage Biometrics at the Rijksmuseum: dating and tracing the origin of silver objects
Type: Master EE/M/CS
Student: Unassigned
Duration: TBD (min. 6 months/fulltime)
If you are interested please contact:
Project overview:
The Rijksmuseum (Amsterdam) has a large collection of silver objects. All Dutch silver is hallmarked: maker, city, and year are known. So far, experts at the museum used Partial Least Square Regression (PLS) for dating silver objects based on the metal composition. Their current PLS model, which is based on 12 metals, is far from perfect.
Research Questions
- Is it possible to develop a model for dating silver based on the composition that outperforms current state of art?
- From which regions such silver objects come from? Who is the maker?
Why Join?
- Be part of a high-impact project that has the potential to substantially contribute to heritage science.
- Work at the intersection of Machine Learning / Deep Learning and conservation.
- Collaborate with a team of interdisciplinary experts of engineers, computer scientists, and conservators from the Rijksmuseum.
Who Should Apply?
You are a graduate student with experience in Computer Vision and Machine Learning / Artificial Intelligence. You should be a capable programmer with prior experience in using Python. The project requires critical thinking and exploring new directions, so you will also have the opportunity to go beyond current approaches. Affinity or interest in the world of decorative arts is expected. Enthusiasts of computer vision for decorative arts, seeking to make a tangible impact in heritage science.
Co-supervisor:
- Joosje van Bennekom (senior metal conservator, Rijksmuseum)
- dr. ir. Alexia Briassouli (assistant professor, DMB-EEMCS)
- Gerben Mooiweer (data analyst and former teacher chemometrics at Radboud University/Wageningen University)