The PhD candidate will contribute to the research project “iREACT – IntegRativE biology approach to understand Adaptive physiology of bovine reared on grass to produce low Carbon meaT”. Grass-fed cattle production should be considered in the context of a circular economy, as well as for local and global food security but remains a challenging task for farmers. Grass availability remains seasonal, uncertain, and sensitive to climate changes, resulting in disrupted cattle production itineraries and the discontinuous availability of nutrients. For beef cattle, this means that maintaining a satisfactory level of growth performance without compromising animal health and welfare or meat quality is a priority.
The rationale of the iREACT project is that beef-on-dairy crossbred cattle combine high efficiency and resilience capabilities due to adequate balance between fat tissue relative to muscle depositions (fat-to-lean ratio) and consequently mobilise body energy reserves when faced with periods of reduced feed availability. The PhD candidate will focus on the development of imaging techniques and mechanistic model for monitoring and predicting the fat-to-lean ratio during heifers' growth.
You have a background in animal science, nutrition, physiology, bioinformatics or a related field, with a strong interest in applied research and new technologies in agriculture. You are interested in precision livestock farming, beef meat production system and in adaptative physiology of cattle. You are interested in combining animal experimentation with computational methods. You have good computer skills and some experience in handling data and models, as well as scientific writing skills, along with motivation to further develop these skills.
iREACT is a SNF and ANR funded project, conducted within a strong research network involving Agroscope and French institutions (INRAE UMRH and UMR PEGASE, Inria and IMT). The PhD candidate will additionally benefit of academic supervision at University of Fribourg offering an interdisciplinary and applied research environment at the interface of animal nutrition and data sciences.
