Do you want to make a significant impact by preventing Infections in poultry? Then you have a part to play! As a PhD candidate, you will be part of the group at the Life Science Trace Detection Laboratory of Radboud University. The work is within the NWA-ORC OBSeRVeD project on the early detection of poultry disease using chemical profiling.
Infections in poultry are accompanied by the emission of specific volatile organic compounds (VOCs), as confirmed by veterinarians who can smell the presence of disease in a stable. Currently, many infections are not discovered until after widescale spread within or between flocks. At a late stage of detection, when most of the damage has already been done, it is also too late for preventive interventions, so that more rigorous treatments of entire flocks with antibiotics or chemicals are required. These treatments can induce antibiotic resistance and leave residues in poultry products and the environment impacting both animal and human health. Hence, there is a need to identify poultry disease at an early stage to reduce the use of antibiotics and financial loss for farmers and society. Within the NWA-ORC project OBSeRVeD, you will be working in a multidisciplinary team using state-of-the-art mass spectrometry instrumentation and multivariate analysis strategies to identify the VOCs fingerprint of the targeted poultry diseases. This information will serve other partners in the project to develop a selective and sensitive system (i.e. e-nose) to detect the presence of the targeted poultry diseases. Your teaching load may be up to 10% of your working time.
Your role and responsibilities will include, but are not limited to, the development and implementation of VOCs sampling strategies using various samples (air, faeces, swabs, litter) at research facilities and poultry farms; the design of experiments to identify disease-specific VOCs; the analysis of collected VOCs samples by mass spectrometry (TD-GC-MS, PTR-ToF-MS) data processing; the identification of VOC signatures of poultry disease through multivariate analysis / chemometric models; and the validation of eNose output with golden standard instrumentation. You will work within an interdisciplinary team of university and industrial partners.