Agriculture and Environment Natural Resources

Area/s: Agriculture and Environment | Natural Resources

Organization: Universitat Autònoma de Barcelona

Research theme code : RLA-UAB-04

UAB invites applications for a postdoctoral fellowship contributing to cutting-edge research in animal well-being assessment using machine learning. Fellows will work on developing AI models for monitoring and improving the quality of life of animals in various contexts, including agriculture, wildlife conservation, and animal husbandry. Ideal candidates will have a background in AI and eager to drive innovations with real-world industrial applications.

1.Challenges in Animal Well-Being Assessment:

  • Multimodal Data: Assessing animal well-being involves analysing diverse data sources, such as images, videos, sensor readings, and behavioural patterns.
  • Subjectivity: Traditional assessment methods often rely on human observers, introducing subjectivity and potential bias.
  • Scalability: With the increasing need for large-scale monitoring, manual assessment becomes impractical.

2. Benefits of Machine Learning:

  • Automated Analysis: ML models can process large volumes of data efficiently, automating the assessment process.
  • Objective Metrics: ML-based approaches provide objective metrics, reducing reliance on human judgment.
  • Early Detection: ML algorithms can identify subtle changes in animal behaviours or health, enabling early intervention.

The selected fellow would be integrated in the Department of Information and Communications Engineering at the UAB, within the research group on Interactive Coding of Images (GICI). The selected fellow would collaborate with several partnering institutions around the world (including zoos, farms, aquatic parks, …).
Fellows can also suggest some other application venue, as long as it is related to animal wellbeing.

Key Responsibilities:

  • Design and develop AI models for animal wellbeing.
  • Research and design algorithms for real-world applications.
  • Supervision of PhD students.

Qualifications:

  • PhD in Computer Science, Data Science, Mathematics or related fields.
  • Strong knowledge of AI techniques, including deep learning, image processing.
  • Interest in interdisciplinary collaboration at the intersection of technology and real-world applications.
  • Strong collaborative skills.

Principal Investigator: