Although quite recent, the adoption of Machine Learning (ML) techniques for the analysis of archaeological data sets is rapidly increasing. ML applications deal with numerical and/or categorical data, textual data, images and geospatial data. Applications on bioarchaeological data, such as plants and animal remains, are still underdeveloped, although some works on agriculture, marine resources and taphonomy of animal bones are gradually intensifying. Thus, the present PhD project is an opportunity for the development of original ML solutions to boost the impact of AI in archaeozoology and offer new perspectives to researchers in that field. The inspection of faunal remains, i.e. animal bones found in archaeological contexts, provides researchers with information on past human-non-human mammal relationships, paleoenvironments, past animal populations (biology) and on the subsistence economy of ancient societies.
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