36-months Double Degree PhD Scholarship : Monitoring of grazing animals using sensors and data science

Organisation/Company

Université de Liège

Department

Gembloux Agro-Bio Tech

Research Field

Agricultural sciences

Researcher Profile

First Stage Researcher (R1)

Positions

PhD Positions

Application Deadline

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

Country

Belgium

Type of Contract

Temporary

Job Status

Full-time

Is the job funded through the EU Research Framework Programme?

Horizon Europe (other)

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

No

Context: Grasslands cover a significant share of the world’s ice-free land mass and are at the heart of the most criticized as well as most sensitive livestock farming systems. Adequate management is of utmost importance to maintain pasture health and allow the grasslands to provide these ecosystem services in the best possible way contrariwise, poor management leads to depletion of the forage resource with a whole cascade of negative effects for both the grazier and the environment. Grazing is a process that has declinations at multiple scales in space and time ranging from the whole paddock over a grazing season to the smallest unit of the grazing process, i.e. the grass-severing bite, that covers a couple of cm². Herbivores continuously sense the ever-changing grazing environment in order to adapt their decisions. Short-term decisions made at the level of each individual bite have consequences on the efficiency of the grazing process, the performance of the herbivores and the health of the grasslands. As theorized by Charnov & Orians, herbivores are optimal foragers able to consume forage at higher rates than what the average sward structure would allow them to. Hence, starting from a favourable sward structure, the efficiency of the grazing process usually decreases with grazing down level: the lower animals get in the vegetation, the lower the harvest per bite. As a consequence, herbivores will increase the amount and/or the frequency of bites, will change the duration of their meals and will gradually have to cover higher areas during their meal sessions to look for these optimal structures until the sward is so depleted that they don’t waste time looking for better sward structures that they consider are no more present on the paddock. Therefore, a better continuous monitoring of the perception of the animal behaviour on field is an open door to develop tools to spot animal or grassland health problems or to analyse reactions to specific structural elements in grasslands to innovate in grazing management. Over the past decades, many studies have documented the potential of sensing technology to monitor the grazing behaviour of domestic herbivores which served as the first bases during the implementation of this project. Accelerometers and Inertial Measurement Units (IMU) combine practicality and good sensing performances for the inference of a wide range of behaviours, activities and postures, possibly down to the level of the bite. Other sensors, such as microphones and pressure sensors have been explored but present some limitations in the range of behaviour they can supply and situations where they can be used. Global Navigation Satellite Systems (GNSS) combined to real-time kinematics (RTK) technology give complementary information to accelerometers and have been frequently used to monitor free-ranging animals. The location of the device on the animal also varies. While most works locate the sensors on halters, close to the jaws or on the neck, farmers are used to put collars on their stock and not halters. But PhD Position H – Monitoring of grazing animals using sensors and data science most importantly, the weaknesses that have been evoked in the conclusion of most work is the unverified or lack of adaptability of models developed in specific case studies to new environment. 

Objectives: The PhD research will contribute 1) to the analysis methods and metrics for understanding the complex interactions between forage resource and dynamics; 2) to develop Machine Learning methods for analysing sensor data on animal movement and behaviour, adapting methods to different animals and environments; 3) to develop methods for cleaning and integrating data from different types of sensors Work plan and task scheduling: 1. Conduct a literature review on the data collection techniques, data preparation and characterization, and machine learning techniques that can be used to analyse the data [Month 1—6]. 2. Familiarize with databases consist of synchronized video-taped animals (“ground truth”) and wearable sensor-based data (GNSS, 3-D inertial measurement units) worn by grazing herbivores in the framework of grazing studies. [Month 6 – 12] 3. Develop a machine learning model and train it on the existing datasets, [Month 12 – 18]. 4. Collect more data, during the project from one or two grazing experiments to complement the datasets with key conditions that will have been identified as missing in the existing databases. This will help improve the quality of the datasets and therefore of the learning model. [Month 12 – 30]. 5. Implement the system and validate its performance on two different datasets coming from different regions of the continent. Assess its prediction reliability. [Month 24 – 33] 

Expected Results 1) Methods for analysing sensor data on animal movement and behaviour; 2) Standardized data storage format established; 3) Methods for addressing data inconsistency from various sources through cleaning

Website

https://www.eu4greenfielddata.eu/phd-positions-application/list-of-phds

Research Field

Agricultural sciences

Education Level

Master Degree or equivalent

● MSCA Mobility Rule: researchers must not have resided or carried out their main activity (work, studies, etc.) in Belgium for more than 12 months in the 36 months immediately before their date of recruitment ● All researchers recruited in a DN must be doctoral candidates (i.e. not already in possession of a doctoral degree at the date of the recruitment) ● An applicant must have received the equivalent of 300 ECTS with a major in computer science or agricultural engineering or equivalent, from which at least 120 ECTS corresponds to a master degree. The master degree must be granted by a university recognized by the International Association of Universities. ● Scientific excellence to fit the PhD project ● Fluent (oral and written) English skills as the project operates in English language ● Knowledge of the language of the host country may be considered a merit (French and English) ● Team-mindedness

Website for additional job details

https://www.gembloux.uliege.be/upload/docs/application/pdf/2026-01/monitoring_o…

Number of offers available

1

Company/Institute

Gembloux Agro-Bio Tech, Université de Liège

Country

Belgium

City

Gembloux

Geofield

Number of offers available

1

Company/Institute

University College Dublin

Country

Ireland

Geofield

City

Liege

Website

http://www.uliege.be

Street

Place du 20 août, 7

Postal Code

5030

E-Mail

jerome.bindelle@uliege.be

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