PhD position: Characterization of abiotic stress of trees using AI methods on acoustic signals

Organisation/Company

University College Dublin

Research Field

Computer science » Informatics

Researcher Profile

First Stage Researcher (R1)

Positions

PhD Positions

Application Deadline

15 Apr 2026 - 00:00 (Europe/Paris)

Country

Ireland

Type of Contract

Temporary

Job Status

Full-time

Offer Starting Date

1 Sep 2026

Is the job funded through the EU Research Framework Programme?

Horizon Europe - MSCA

Marie Curie Grant Agreement Number

101226371

Is the Job related to staff position within a Research Infrastructure?

No

Exceptional benefits at a glance 

•  International PhD training excellence (here)
• Renowned supervisors & top-tier labs
• Interdisciplinary & multi sectoral research
• Competitive MSCA salary & allowances
• Global academic & industrial network
• Non-academic secondments 

Salary Gross amount (per month)

Living Allowance  EUR 5470

Mobility Allowance EUR 710

Family Allowance EUR 660 

GreenFieldData Project at glance : “IoRT Data management and analysis for Sustainable Agriculture” is a project funded under the action HORIZON Marie Sklodowska-Curie Action (MSCA) Joint Doctoral Network. GreenFieldData will train a new generation of researchers able to tackle digital and green transition challenges using a human-centric approach to ensure the robustness and relevance of the solutions responding to the specific needs of the EU market in a context of climate change and increasing socio-economic constraints. 

https://www.eu4greenfielddata.eu

 GreenFieldData will mobilize 14 Doctoral Candidates (DCs) enrolled in Double Degree Doctorate programmes with 12 academic main beneficiary partners, across 7 EU countries. Moreover, 21 non-academic associated partners, and 3 academic associated partners will provide support to the DCs. 

PhD Position G – Characterization of abiotic stress of trees using AI methods on acoustic signals 

All details here: https://www.eu4greenfielddata.eu/content/download/201/2097?version=3

Context: 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. Different sources of acoustic emissions have been identified (e.g. air bubble formation in conductive tissues, cell lysis, mechanical rupture, see references below) generating acoustic signals with their own characteristics. The analysis of the waveforms (amplitude, frequency, etc.) allows them to be discriminated under single stress conditions. However, to date, no study on a set of stresses (succession or interaction) has been carried out, and since plants are permanently subjected to different stresses, the use of this technique remains limited (in time, e.g. period of water stress, or in space, e.g. altitudinal limit). This case study therefore aims to better characterize the acoustic emissions generated by a single constraint and by their interactions, in order to ultimately develop a tool capable of measuring damage under natural conditions. 

Objectives: This case study will 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). The characterization of the acoustic signature will make it possible to measure the damage generated by different climatic hazards and to better understand the physiological mechanisms of resistance to abiotic constraints. The acoustic signature, integrated into the algorithm controlling the autonomous acoustic sensors, will make it possible to trigger alerts and an adapted response to these different climatic constraints. The design of a tool capable of measuring damage and, ideally, mitigating its consequences before it becomes irreversible is key to mitigate consequences of climatic stress. By providing a better understanding of the physiological mechanisms that plants develop to resist abiotic stress and, above all, their interactions, it fits to the challenge agroforestry and agro-ecology will face in the future. All the mentioned objectives can be listed as follow: 1. Investigate the potential of using acoustic emissions to detect and measure damage caused by abiotic stresses (drought, frost, etc.) in plants; 2. Develop a non-invasive method for continuous plant health monitoring based on reliable acoustic signatures; 3. Analyse the unique acoustic signatures of different abiotic stresses on plants by means of advanced analytics methods. This is a novel approach as previous research focused on single stresses, while in nature plants experience multiple or interacting stresses. 

Work plan: 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 – 6]. 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 – 6]. 3. Explore the diversity of signals and perform complementary experiments to finalize the training dataset [Month 6 – 12]. 4. Develop a data analysis process based on machine learning for multimodal datasets and evaluate its performance and robustness of it results [12 – 24]. 5. Develop and implement an intelligent acoustic system and validate its results in the field (real-world data). [Month 24 – 33]

Expected Results 1. Ability to detect acoustic signals and measure damage caused by different climate hazards on plants in controlled conditions; 2. AI methods for collecting large, representative, high-quality acoustic datasets over time; 3. AI methods for removing noise and outliers from the data; 4. AI methods for extracting key features from multi-modal data; 5. AI assisted monitoring of damages in the field.

PRACTICAL INFORMATION 

Recruiting and host institutions 

● University College Dublin, National University of Ireland, Dublin, Ireland (18 Months) (Recruiting institution) 

● INRAE, Clermont-Ferrand, France (18 Months) Doctoral schools 

Supervision

● Pr. Tahar Kechadi (University College Dublin, Ireland) 

● Dr. Guillaume Charrier (INRAE, France) 

Non-academic mentors 

● Mr. M. Connolly (M2Geo, Ireland) 

● Dr. A. Proust (Mistras, France) Secondments (1 to 6 hosting months) 

Contact information

 ● tahar.kechadi@ucd.ie ● guillaume.charrier@inrae.fr

How to apply

https://www.eu4greenfielddata.eu/phd-positions-application/how-to-apply

Website

https://www.eu4greenfielddata.eu/

Research Field

Computer science » Informatics

Education Level

Master Degree or equivalent

Languages

ENGLISH

Level

Good

Number of offers available

1

Company/Institute

University College Dublin

Country

Ireland

City

Dublin

Geofield

Number of offers available

1

Company/Institute

INRAE

Country

France

City

clermont-ferrand

Geofield

City

Dublin

Website

https://www.eu4greenfielddata.eu/

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