Wageningen University & Research’s Animal Sciences Group (ASG) is launching a new investment programme centred on the digital transformation of the animal sciences. The programme initiative aims to further strengthen knowledge and methods in data-driven research, data science and artificial intelligence (AI). Seven PhD tracks form the core of the programme and will be carried out jointly by Wageningen BioVeterinary Research (WBVR), Wageningen Livestock Research (WLR) and Wageningen Marine Research (WMR), in close collaboration with the graduate school WIAS and Wageningen University.
The PhDs work under a single theme: “Digital Biology – Integrating Behaviour, Health and Biodiversity in Animal Science”.
The ‘Digital Biology’ programme focuses on developing and applying digital technologies (AI, sensors, modelling, data fusion) to better understand and predict the behaviour, health and biodiversity of animals.
Together, the PhD candidates strive for a better understanding of the ‘digital physiology’ of animals: how behaviour, , pathogens, health and environmental stimuli jointly determine how animals function, adapt and contribute to ecosystems.
PhD: Development of AI Models for prediction of resilience and susceptibility infectious diseases and vaccine efficacy. How can advanced mathematical modeling, combined with time series of non-invasive sensing technologies and multi-source biomarker data, be used to develop non-invasive measurable biomarkers that accurately predict the resilience of animals in relation to infectious diseases?
The assessment of disease susceptibility (i.e. whether animals get infected) and disease resilience (i.e., the clinical severity in short and long term) in animals currently relies on infrequent or invasive sampling methods that do not capture rapid biological changes. Biomarkers (including sensors and blood-related markers), —quantifiable indicators of physiological or pathological processes—offer a promising route for real-time physiological assessment. Advances in non-invasive sensing technologies and machine-learning-driven analyses create opportunities for high-frequency, minimally invasive measurements. Proof of concept will be used in sheep, cattle or pigs, initially based on data from clinical eperimental trials and later at farm leve.
You will:
- Identify measurable and easily accessible biomarkers and sensors indicative of infectious and health status and disease progression in animals.
- Integrate multiple data sources (non-invasive measurements, internal biomarkers, pathogen load) to create robust datasets for modelling.
- Develop mathematical and machine-learning models that predict clinical outcomes and disease progression.
With this, we will move on to advanced AI modeling and the integration of different data sources and approaches—statistical modeling, machine learning, and the integration of multi-omics and sensor data—to build new predictive tools to predict resilience for infectious diseases.
You will work hereThe research is embedded within Wageningen Bioveterinary research located in Lelystad embedded with the chair
Adaptation Physiology of the animal science group , and topic resilience of animals, which is led by
Prof. Annemarie Rebel .