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/Brussels) 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? NoOffer DescriptionExceptional benefits at a glanceInternational PhD training excellence (here)Interdisciplinary & multi sectoral researchCompetitive MSCA salary & allowancesGlobal academic & industrial networkNon-academic secondmentsSalary Gross amount (per month)Living Allowance EUR 5470Mobility Allowance EUR 710Family Allowance EUR 660GreenFieldData 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.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: Data collection and analysis empowered with AI for robotized Olive Oil Precision FarmingContext: Olive production has a tremendous economic and cultural impact on European agriculture. Portugal alone is the sixth largest producer of olive oil in the world and fourth in Europe. The current trend in agricultural practice is to transform olive orchards into intensive or semi-intensive layouts. Water is usually scarce in these regions, so improving irrigation in olive orchards is critical, given both production and environmental preservation. Another factor to consider is weed control. The main methods are mechanical (soil tillage) and chemical (spraying). These operations increase the costs of the olive grove but also impact soil health (interfering with soil biodiversity and its organic carbon stock), contributing negatively to carbon emissions and the degradation of organic matter (soil fertility). We have developed an autonomous ground scouting robot that continuously monitors soil, weeds and plants at a proximity level to the ground and plants close to 30cm in the row spaces underneath the olive trees. This approach contrasts with drone or satellite imagery, which is limited in viewing the in-row ground due to the blocked line of sight. Ground stations also do not provide an optimal solution since they are fixed in space, providing a relatively small spatial resolution. Only with ground autonomous robots can the ground underneath olive tree rows be accurately and practically monitored since it does not require extra human labour. In the near future, we envision multiple networked collaborative ground robots performing scouting and data acquisition tasks in a sustainable way, both environmentally and economically.Objectives:This PhD research project will focus on three main objectives:Engage farmers and association technicians, to target and deploy cost-effective data collection technology and develop innovative context-awareness algorithms for monitoring olive crop and AI techniques, adaptive enough to deal with the spatial evolution of organic matter and soil.Develop an unified process in managing robotized precision agriculture methods (making informed decisions for timing and resource allocation, in weeding operation planning, fertilizer applications, water irrigation, etc.).Provide improved decisions to optimize resources and yields, adapting to changing environmental conditions.Work plan:Conduct a literature review on cost-effective and tuned data collection technology to monitor the crop in real-time (Month 1 – 6).Develop a technique for robotic data collection, ensuring its performance and anticipating the degradation of water and nutrient supplies (Month 3 – 6).Develop efficient multimodal and context-awareness ML algorithms for smart farming monitoring. (Month 6 – 12).Develop and implement adaptive spatio-temporal ML algorithms to deal with spatial evolution of organic matter and soil pH (12 – 24).Implement an olive smart farming framework for providing informed decisions of key farming operations. (Month 24 – 33)Expected ResultsCost-effective and tuned data collection technology to monitor the crop in real-time, ensuring its performance andanticipating the degradation of water and nutrient supplies.Efficient multimodal and context-awareness ML algorithms for smart farmingmonitoring.Adaptive spatio-temporal ML algorithms to deal with spatial evolution of organic matter and soil pH.Olive smart farmingframework for providing informed decisions of key farming operations.Recruiting and host institutionsUniversity College Dublin, National University of Ireland, Dublin, Ireland (18 Months) (Recruiting institution)UCD SGS@UCD, National University of Ireland, Dublin, Ireland.Pr. Tahar Kechadi (University College Dublin, Ireland)Number of offers available 1 Company/Institute Instituto Superior Técnico, University of Lisbon Country Portugal City Lisbon Geofield
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