The AXIAUM project focuses on raising innovative interdisciplinary research topics on health and environment related to artificial intelligence (AI) with the scope of developing both new methodologies and finalized research. The ambition of the programme proposed by the consortium University of Montpellier/IMT Mines Alès is to strongly link new research on AI to the existing research and the AI strategy of the consortium, reinforced by the current structuration of data science within the ISDM Montpellier Institute of Data Science. The project is closely tied to the MUSE I –Site certification obtained by the UM in 2017 for its project "Montpellier University of Excellence" (MUSE). The project addresses three major 21st century challenges, namely: to FEED, PROTECT, and CARE. The topics of the AXIAUM project cover 4 main domains: • Health; • Environment; • Foundations of Artificial Intelligence: ontology alignment, constraint • Social Sciences. The selection of the subjects and PhD candidates will follow a two-step process based on rigorous eligibility criteria: excellence, interdisciplinary, intersectoriality, internationality, ethics and coherence with the AI strategy defined by the consortium. Research works on Health will in particular address cancer and HIV. Deep learning methods will be investigated and extended in order to treat IRM images. More generally, mathematical approaches and machine/statistical learning will be developed, (metabolic plasticity and heterogeneity in melanoma, dynamic statistical networks for cancer cells, use of genomics and Immunogenetics,…) One of the goals is to design and develop a novel analysis framework for large scale OMICS and multiscale data analysis in health. Digital Health in Motion will also be investigated to better understand the ethology of human movement (development and clinical validation of "motion data"). Machine learning and deep learning will also be investigated and novel methods will be built in interdisciplinarity for environmental challenges such as costal and marine biodiversity monitoring, flooding risks and karstic, etc. The works will be based on heterogeneous massive data as for instance high-resolution satellite images. Indeed, coastal marine ecosystems shelter a high biodiversity. By measuring, using imagery and deep learning, one topic of the AXIAUM project is to propose novel monitoring methods of underwater biodiversity. The research topics may address several topics, such as the identification of determinants of water transport in plants through pattern recognition, the automatic and high throughput plant organ recognition (root development), the determination of the network of interactions between plants and pollinators by identifying pollen grains transported by captured insects, etc. Foundations of artificial intelligence will be considered by studying research topics like the study of the complexity of constraint acquisition by developing a taxonomy of queries for constraint acquisition, over-fitting to learning data, noise and sparsity robustness, etc.. Within the scope of social sciences, we propose to try and understand how artificial intelligence will have the effect of shaking up the traditional conception of creator and check if the condition of the existence of a creator (physical person) is a condition of application of the intellectual property law. On another view, online marketplaces are evolving with artificial intelligence. We aim at understanding the legal issues of data analysis in the functioning of online marketplaces, and the techniques used to model the contractual relationship between, on the one hand, the operator of the platform and the sellers and, on the other hand, between the sellers and the buyers.