Precision livestock farming (PLF) is defined as the individual animal management by continuous real-time monitoring of health, welfare, production/reproduction, and environmental impact.The use of sensors to collect data on animals’ behavior and livestock farming production in PLF has several potentials, including: i) the early detection of diseases and other animal welfare issues; ii) the improvement of production performances; iii) the optimization of natural resources usage; iv) the minimization of environmental impact; v) the increase of livestock farming societal acceptance. The proposed research project aims to apply artificial intelligence (AI) techniques and methodologies to data collected in real precision livestock farming (PLF) scenarios to experimentally support the achievement of PLF objectives and potentials. Specifically, the research goal is to find the best trade-off between livestock farming productivity, natural resources usage, animal welfare and environmental impact, while improving food quality, guaranteeing food safety, and favoring the adaptation and mitigation to climate change. Historical and new data collected in PLF systems will be analyzed by means of AI techniques and methodologies, with the aim of developing data analysis and prediction models able to determine the best balance between animal nutrition, emission of climate-altering substances and sustainable production requirements.

The preferred candidate for conducting the proposed research should have the following knowledge and skills:- Knowledge of machine learning and deep learning algorithms and techniques for the supervised and unsupervised learning of predictive models on heterogeneous, sparse and noisy data, including data in the form of time series.- Programming skills with python and AI libraries and platforms.- Experience with SQL and NoSQL databases and Web programming.- Propensity to team working and interdisciplinary research.The preferred candidate should possibly also have knowledge of precision livestock farming methodologies and technologies.

The proposed research will be developed at the University of Rome UnitelmaSapienza in cooperation with the Research Center on Animal Production and Aquaculture of the Italian Council for Agricultural Research and Economics (CREA) under the supervision of Prof. Damiano Distante, PhD (SSD INF/01, ERC: PE6_7, PE6_10, PE6_11), University of Rome UnitelmaSapienza, in collaboration with the following researchers:- Prof. Stefano Faralli, PhD, Computer Science Department, Sapienza University of Rome;- Dr. Miriam Iacurto, Dr. Roberto Steri and Dr. David Meo Zilio, CREA Research Center on Animal Production and Aquaculture (agreement between UnitelmaSapienza and CREA to be signed).