This course is offered in collaboration with the PhD School in Animal and Food Science

Period

31th Agust - 4th September 2026

Location

Agripolis Campus (Viale dell'università, 16. 35021 Legnaro - PD)

Google maps link

Instructors
Dr. Guilherme J. M. Rosa (link), Professor at the Department of Animal & Dairy Sciences and Department of Biostatistics & Medical Informatics, University of Wisconsin Madison

Dr. Joao Dorea (link), Professor at the Department of Animal & Dairy Sciences and Department of Biological Systems Engineering Informatics, University of Wisconsin-Madison.

Registration at this link

Registration fees
·       250 € for students, PhD students, post-doc and all staff in training
·       350 € for researchers, professors and all tenure track academic staff

Organization / contacts

Please forward your requests of information to Alessio Cecchinato (alessio.cecchinato@unipd.it)

Course Description

This graduate-level course (PhD and advanced MS) is designed for researchers and professionals in animal sciences, including nutrition, physiology, management, genetics, and reproduction, working in academia or industry, particularly those interested in data analytics, artificial intelligence (AI), and precision livestock systems. The course is structured with four sessions per day from Monday to Friday (with Wednesday afternoon reserved for discussion and networking), combining lectures with hands-on demonstrations using real data, software tools, and algorithms that participants can directly apply in research and industry R&D settings.

Target audience and prerequisites
Graduate level course (PhD and advanced MS) for researchers working in all areas of animal sciences, such as nutrition and physiology, management, genetics and reproduction, in industry or academia. Statisticians, computer scientists, and data scientists interested in learning about potential applications in animal science can also benefit from the course.

Topics

• Big Data and Data Science in Livestock Systems

• Artificial Intelligence in Animal Agriculture
• Planning Research Studies in Data-Intensive Animal Science
• Wearable Sensing Technologies
• Infrared and Hyperspectral Imaging
• Acoustic Monitoring Systems
• Mining Operational Farm Data
• Machine Learning Techniques for Animal Science
• Multidimensional Regression and Classification
• Deep Learning Architectures (CNNs, RNNs, Transformers)
• Large Language Models in Animal Science Applications
• Genomic Prediction and High-Dimensional Genomic Data
• Image Processing and Computer Vision for Livestock
• Multimodal Data Integration
• AI for Precision Nutrition and Health Monitoring