Case Western Reserve University is seeking candidates for the newly established D. Keith and Margaret B. Robinson Post-Doctoral Fellow in Data Science in Art. The Fellow will work with a broad interdisciplinary team, drawn from the Departments of Physics and Art History at Case Western, the Cleveland Museum of Art, the Cleveland Institute of Art, and Factum Arte in Madrid. The project involves the application of machine learning on painted artworks to attribute the hand of the painter based on surface topography images of the artist's brushstrokes. It builds on a recently completed controlled study (https://doi.org/10.1186/s40494-021-00618-w) that demonstrated the efficacy of this technique as a reliable method of attributive connoisseurship.
The next stages of the collaboration will focus on: i) attribution of sections in the later paintings of the Spanish Renaissance artist El Greco using surface profile data scanned by Factum Arte; ii) design and analysis of a new controlled experiment that mimics historical workshop practices, with multiple painters contributing to the same work; iii) development and application of supervised and unsupervised machine learning techniques that analyze surface topography along with a variety of complementary data, including x-ray scans and photos. For any questions about the position, please contact Profs. Ken Singer (kds4case.edu) and Michael Hinczewski (mxh605case.edu).
Applications will be evaluated on a rolling basis starting 1/3/22 until the position is filled. We are open to candidates working with us either on location at Case Western or remotely.
Ph.D. with research experience in any related area of expertise, for example technical art history, data science, physics, or material science. The candidate should have familiarity with machine learning techniques, broadly defined, as demonstrated through their own earlier research projects.
To apply, go to: https://academicjobsonline.org/ajo/jobs/20847
JOB: D. Keith and Margaret B. Robinson Post-Doctoral Fellow in Data Science in Art. In: ArtHist.net, 07.01.2022. Letzter Zugriff 06.07.2022. <https://arthist.net/archive/35615>.