Contributing to documenting the dynamics of interactions between humans, wild and domestic fauna, and environments during the Holocene, this project aims to improve the identification of ancient fauna. It will strengthen the study of anthropogenic impact on past biodiversity and its transformations, and refine the recognition of extinct animal varieties or those with particular adaptations by combining methods borrowed from archaeozoology, 3D imaging and machine learning.

The main task of the successful candidate will be to help redefine certain traditional criteria of comparative anatomy used in archaeozoology and to establish new criteria specific to 3D models, based on 150 3D-modelled taluses belonging to five morphologically similar taxa: sheep, goats, alpine ibex, European roe deer and gazelles. These criteria will form the future reference features for machine learning (AI) algorithms. The successful candidate will work as part of a multidisciplinary team of archaeozoologists and mathematicians.

The mission will be based at the CEPAM laboratory, part of the Maison des Sciences Humaines et Sociales Sud-Est (MSHS) at the Université Côte d'Azur (UniCA) in Nice (Saint Jean d'Angély 3 campus). The doctoral candidate will be co-supervised by Manon Vuillien (archaeozoologist, CNRS) and Marco Corneli (CPJ, UniCA).