Development of novel methodologies and models for milk quality monitoring (PhD) (ref. BAP-2023-199)
Investing in sustainable food production and food security is more relevant than ever before. Livestock farming is an important link in this process because animals have the ability to convert low-value nutrients, vegetable fibers and residual flows into high-quality and nutritious proteins. However, for livestock farming to be sustainable, the production needs to be efficient and animals have to be healthy and resilient and kept under good conditions to guarantee animal welfare. With the help of technology, objective and detailed data about the performance and behaviour of individual animals can be collected and processed by the most up-to-date machine learning algorithms, to select the most efficient and resilient animals, monitor their health and welfare, and provide the farmer with tools to optimize animal caretaking.
The “Livestock Technology” research team, under the lead of Prof. Ben Aernouts, develops, implements and validates innovative sensor technology and machine learning algorithms to support animal management in livestock farming. Our research team operates from the KU Leuven campus in Leuven and Geel and works in close collaboration with research farms, the livestock sector and food processors.Website unit
Optical sensors are widely used in the agro-food sector to measure and monitor product quality. At the Livestock Technology research group of KU Leuven, we develop innovative and customized near-infrared spectral sensors to measure the composition of raw milk whilst being extracted from the cow’s udder. The obtained information can be used in real-time to separate milk based on its properties, safeguarding the quality of the delivered milk, or to create milk batches with unique characteristics. Moreover, as the production of milk is a dominant factor in the metabolism of dairy cows, involving a very intense interaction with the blood circulation, the extracted milk contains valuable information on the health status of the cow. As a result, the analysis of the milk components for each individual cow can provide valuable information on the cow’s udder health and metabolic and nutritional status. This can support early detection of altered health and welfare and reduce the use of antibiotics by taking preventive actions.
As all process analytical technologies, our sensors and their measured spectral data are subject to drift and structural noise. These discrepancies can originate from small differences in the hardware of sensor replicates, wear and maintenance, environmental fluctuations or variations in the cow management. In the past years, novel chemometrics methodologies and machine learning algorithms have been developed to account for these effects or obtain models that are more robust against these sources of drift.
The main goal of this applied project is to implement, optimize and validate different strategies for robust calibration, calibration transfer and calibration maintenance so that the models to predict the milk quality traits from the measured spectral data can cope with changing environments and conditions. Furthermore, the robustness of these prediction models will be evaluated and improved with the vast amount of on-farm data and experience that we have available. Finally, the variation of the sensor measurements and the outcome of the models will be studied in relation to the milk quality and cow health and combined with advanced data-processing techniques to obtain robust monitoring and early-warning systems.
To perform this practice-oriented research, the Livestock Technology group of KU Leuven is looking for a highly motivated PhD candidate.
- hold a MSc degree (with minimal distinction and obtained in the last 3 years at a university in the EEA) in (bio)statistics, chemometrics, artificial intelligence, bioscience, bioscience-engineering or equivalent
- are eager to perform research on the crossroads of bioscience, data science, process analytical technologies and precision farming
- have a creative, critical, analytical and innovative mindset
- have good oral and written communication skills in English
- are eager to work in a multidisciplinary and diverse team of national and international researchers and learn and explore innovative technologies
- have a strong interest in technology, machine learning and scientific research
- are interested in building a career in data science
Then you are THE candidate we are looking for and we would like you to apply for this interesting PhD position.
Experience with chemometrics (PCA, PLS, …), process analytical technologies and scientific data-processing software (such as Matlab, Python, R or equal) is a plus.
You are not eligible for this position if you don’t hold a MSc degree with minimal distinction and obtained in the last 3 years at a university in the European Economic Area (EEA).
We offer a fulltime PhD position with competitive salary for 4 years, preferably starting on the 1st of September 2023 or earlier. Our young, dynamic and multidisciplinary team will support you in all aspects to successfully obtain a PhD degree and proper scientific training at a top-ranked university, with excellent education and learning opportunities.
You will work closely together with the biophotonics and chemometrics research groups of KU Leuven (Prof. Wouter Saeys) and INRAe (Dr. Jean-Michel Roger, Montpellier, France) and the milking technology industry. You will have opportunities to participate in national and international meetings, establish your own network and gain experience in transferal skills.
For more information please contact Prof. dr. ir. Ben Aernouts, tel.: +32 14 72 13 64, mail: firstname.lastname@example.org.You can apply for this job no later than July 17, 2023 via the online application toolKU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.
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