PhD Candidate in Computer Vision for Agricultural Imaging

Develop AI-driven image processing for precision fruit crop management in a collaborative IRTA-UAB research environment.

Best for:Best for computer vision researchers with a strong background in image processing, machine learning, and agricultural applications who seek interdisciplinary collaboration between AI and precision farming.

IRTA (Institute of Agrifood Research and Technology) · Bellaterra/Lleida (flexible), Spain · R1 · First-stage researcher

More professors at IRTA (Institute of Agrifood Research and Technology)

3D image reconstruction in orchardsObject detection for fruit cropsMultispectral image analysisSemantic segmentation of agricultural structuresPrecision agriculture and digital farming

Deadline

Oct 23, 2026

8 days

Funding

Salaried

Not specified (standard IRTA PhD stipend range expected)

Start date

1 January 2027

Full-time

Location

Bellaterra/Lleida (flexible)

Spain

Overview

This PhD position at the intersection of computer vision and agricultural technology offers a unique opportunity to advance digital farming through advanced imaging techniques. The successful candidate will work jointly between IRTA’s Fruit Production Program in Lleida and the Computer Vision Center (CVC) at UAB in Bellaterra, Spain. The project focuses on creating algorithms for 3D image reconstruction, object detection, and multispectral analysis of apple and peach orchards to support data-driven agronomic decision-making. The candidate will contribute to a national research project funded by Spain’s State Research Agency while benefiting from a multidisciplinary environment combining computer vision expertise with agronomic applications. The position emphasizes fieldwork, algorithm development, and knowledge transfer to the agricultural sector, with flexibility to choose between Bellaterra or Lleida as the primary work location.

computer visionagricultural imaging3D reconstructionobject detectionmultispectral analysisprecision farmingAI for agricultureorchard monitoring

What you'll do

Design, execute, and analyze experiments for computer vision-based agricultural imaging under real-world orchard conditions.
Collect and process multispectral and 3D imagery to detect and classify tree structures (shoots, flowers, fruitlets, fruits) in apple and peach orchards.
Develop and validate AI algorithms for object detection, semantic segmentation, and 3D reconstruction tailored to agricultural challenges.
Perform advanced data curation, statistical analysis, and machine learning model optimization for agricultural applications.
Collaborate with agronomists and computer vision engineers to translate research findings into practical tools for orchard management.
Conduct literature reviews and publish findings in international peer-reviewed journals and conference proceedings.
Present research outcomes to stakeholders in the agricultural sector and contribute to technology transfer initiatives.

Requirements

Degree required: Master’s degree in Computer Science, Engineering, or a related quantitative field (e.g., Artificial Intelligence, Robotics, or Agricultural Technology).

Degree offered: PhD

Language: English; proficiency at a level sufficient for academic research and collaboration (equivalent to C1).

Instruction: English

Required skills

Proven expertise in computer vision, image processing, or machine learning algorithms.
Experience with 3D reconstruction techniques (e.g., LiDAR, stereo vision, or multispectral imaging).
Familiarity with object detection and semantic segmentation frameworks (e.g., YOLO, Mask R-CNN, or similar).
Hands-on experience with Python and major ML libraries (e.g., TensorFlow, PyTorch).
Ability to work with large-scale datasets and perform statistical analysis.

Nice to have

Prior exposure to agricultural or environmental data analysis.Experience with fieldwork or sensor deployment in outdoor settings.Knowledge of multispectral or hyperspectral imaging techniques.Familiarity with collaborative research environments or interdisciplinary teams.

Eligibility

No specific nationality restrictions, but eligibility may depend on the funding source of the national project.
Applicants must be eligible to enrol in the PhD Program in Computer Science at UAB.
No age or prior experience caps beyond standard PhD admission requirements.

Funding & terms

Type

Salaried

Amount

Not specified (standard IRTA PhD stipend range expected)

Duration

3 years (standard PhD duration)

Programme

National research project funded by the Spanish State Research Agency (AEI)

Spanish State Research Agency (AEI)

How to apply

Deadline: 2026-10-23 (Europe/Madrid)

Applicants must submit their application through the IRTA talent portal. The process requires a CV, cover letter, academic transcripts, and a research proposal outlining how the candidate’s background aligns with the project goals. Three reference letters may also be requested. The application should be completed in English and directed to the IRTA recruitment team.

Documents required