[PhD 1] Development of Novel Methodologies and Models for Milk Quality Monitoring:
(ref. BAP-2022-399)
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.
Project
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.
Profile
To perform this practice-oriented research, the Livestock Technology group of KU Leuven is looking for a highly motivated PhD candidate.
If you:
- 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).
Offer
We offer a fulltime PhD position with competitive salary for 4 years, preferably starting on the 1st of September 2022. 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.
Interested?
For more information please contact Prof. dr. ir. Ben Aernouts, tel.: +32 14 72 13 64, mail: ben.aernouts@kuleuven.be.
You can apply for this job no later than July 17, 2022 via the online application http://www.kuleuven.be/eapplyingforjobs/light/60127560 tool
[PhD 2] IoT in Agriculture: Development of Novel Sensors for Online Milk Quality and Cow Health and Welfare Monitoring
(ref. BAP-2022-400)
Optimization and intensification of livestock production are widely encouraged to meet the increasing demands for livestock products and to contribute to improving the livelihoods of rural households. To meet this demand, while at the same time keeping dairy farming sustainable and guaranteeing welfare for both the animal and the farmer, we require automation of the production process in combination with thorough health and welfare monitoring of the individual animals.
The research team “Livestock Technology” research team, under the lead of Prof. Ben Aernouts, develops, implements and validates innovative sensor technology and data processing algorithms to support animal management in livestock production. Our research lab is hosted by the KU Leuven campus in Geel, located in a green and rural environment, in close collaboration with research farms, industry and the livestock sector.
Project
In Flanders, dairy production is an important segment of agricultural production, representing about 15% of the total market value. Over the last 50 years, the average milk production per cow and lactation has increased enormously as a result of genetic selection and improved feed and management practices in dairy farming. However, due to the intense focus on high milk production, modern dairy cows are prone to production-related disorders. 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 metabolic status of the cow. Therefore, regular analysis of the produced milk is considered the most efficient way to monitor cow health.
Today, online measurements of milk production, conductivity and color are already common practice in dairy farming. Nevertheless, these milk parameters are influenced by many factors next to the health status of the cow. For this reason, there is a need for monitoring parameters that have a more direct link with the cow’s health. The formation of milk components, such as milk fat, protein and lactose, is the immediate result of the cow’s feed uptake and metabolic status, as well as the health of the milk-producing cells in the udder. As a result, regular analysis of the basic milk components for each individual cow can give valuable information on its udder health and metabolic and nutritional status.
In this project, online milk analyzer prototypes will be designed and built based on miniature spectrometers. These sensors will be implemented in automatic milking systems to monitor the milk quality at the level of individual mammary glands. This will allow for the elimination of systemic effects of the animals through inter-quarter comparison, supporting a high-performance warning system for identifying mastitis. Furthermore, the robustness of these sensors will be evaluated and improved by applying different calibration strategies. Finally, the variation of the sensor measurements will be studied in relation to the cow’s health and combined with advanced data-processing techniques to obtain a robust early-warning system.
Profile
To perform this active, practice-oriented research, the Livestock Technology group of KU Leuven is looking for a highly motivated PhD candidate.
If you:
- hold an MSc degree (with minimal distinction and obtained in the last 3 years at a university in the EEA) in biosciences, bioscience-engineering, engineering (technology), or equivalent
- are eager to perform research on the crossroads of bioscience, engineering and precision farming
- are a hands-on engineer with 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 dairy farming, sensor technology, data processing and scientific research
- are interested in building a career in IoT and 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 scientific data-processing software (such as Matlab, Python, R, C, Labview, or equal) is a plus, as well as experience with statistics and chemometrics.
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).
Offer
We offer a fulltime PhD position with competitive salary for 4 years, preferably starting on the 1st September 2022. 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 research center for dairy production “Hooibeekhoeve” 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.
Interested?
For more information please contact Prof. dr. ir. Ben Aernouts, tel.: +32 14 72 13 64, mail: ben.aernouts@kuleuven.be.
You can apply for this job no later than July 17, 2022 via the online application tool
KU 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.