{"id":164952,"date":"2026-01-27T03:00:49","date_gmt":"2026-01-27T02:00:49","guid":{"rendered":"https:\/\/www.cde.ual.es\/?p=164952"},"modified":"2026-01-26T10:44:23","modified_gmt":"2026-01-26T09:44:23","slug":"phd-characterization-of-abiotic-stress-of-trees-using-ai-methods-on-acoustic-signals-ireland","status":"publish","type":"post","link":"https:\/\/www.cde.ual.es\/en\/phd-characterization-of-abiotic-stress-of-trees-using-ai-methods-on-acoustic-signals-ireland\/","title":{"rendered":"PhD – Characterization of abiotic stress of trees using AI methods on acoustic signals – Ireland"},"content":{"rendered":"

\"vista<\/p>\n

\u201cIoRT Data management and analysis for Sustainable Agriculture<\/strong>\u201d is a project funded under the action HORIZON Marie Sklodowska-Curie Action (MSCA) Joint Doctoral Network.<\/p>\n

Abiotic stresses (e.g. frost, drought, wind) cause significant damage to natural and cultivated plants, which is expected to increase in the future with increasing climate variability (extreme climatic events). The detection of acoustic emissions is a promising way to measure continuously and non-invasively the damage affecting plants.<\/p>\n

This PhD \u00a0will focus on two complementary parts: (i) analysis of acoustic signals to extract relevant information from it (signal quality), (ii) comparison of classified acoustic measurements with ecophysiological reference measurements in cultivated sites with different stress modalities (e.g. agroecological orchards and vineyards along natural gradients).<\/p>\n

Work Plan:<\/strong><\/p>\n

    \n
  1. Conduct a literature review in data collection techniques used to collect the data for this project and ML techniques for multimodal datasets [Month 1 \u2013 6].<\/li>\n
  2. Attend training on database of acoustic signals, applied stress and physiological indices collected in different woody species under drought and frost stress. [Month 3 \u2013 6].<\/li>\n
  3. Explore the diversity of signals and perform complementary experiments to finalize the training dataset [Month 6 \u2013 12].<\/li>\n
  4. Develop a data analysis process based on machine learning for multimodal datasets and evaluate its performance and robustness of it results [12 \u2013 24].<\/li>\n
  5. Develop and implement an intelligent acoustic system and validate its results in the field (real-world data). [Month 24 \u2013 33]<\/li>\n<\/ol>\n

    Requirements<\/strong>:<\/p>\n