PhD position at TU Delft on crop resilience modeling

Become part of the new research institute CropXR! Our program with a budget of 96 million Euro for the next 10 years, aims to develop eXtra Resilient (XR) crops for durable agriculture. Plant biology, simulation modeling, and artificial intelligence are integrated into innovative ‘smart breeding methods’ to develop crop varieties that are more resilient to climate change and less dependent on chemical crop protection. In our interdisciplinary program, four universities and dozens of plant breeding, biotech and processing companies collaborate. Together, we work on basic scientific research, data collection and data sharing, education, and advancing applications in agronomy and plant breeding. If you want to contribute to the development of technology and knowledge for the breeding of crops that are resilient, require no inputs that negatively impact our planet, and contribute to healthy life, then join the CropXR program!

This vacancy is a part of the Crop XR Potato program. The project aims to convert the current theoretical understanding of the physiological underpinnings of the crop resilience and the data from the field experiments into mathematical/statistical models that can be used throughout the potato industry to predict the reaction of potato to stress and control factors. 

As a PhD candidate you will perform research within the CropXR Potato program in the group of Numerical Analysis at the Delft University of Technology. Your work will start with the analysis of the existing physiological models of potato crop resilience and potato-microbe interaction in cooperation with project partners at the Centre for Systems Analysis at the Wageningen University and the Plant-Microbe Interaction Group at the Utrecht University, respectively. These biological models will be used to develop a new type of general mathematical model describing the changes in the joint probability density function of potato phenotype parameters under stress. The resulting mathematical model will be incorporated into a machine-learning model/environment and trained on historical experimental data and the data from the field experiments collected in the present project. The final physiology-informed and data-supported model will be able to predict the resilience of selected potato genotypes and will also help to identify an optimal set of phenotypical and environmental measurements providing robust predictions of resilience at a minimum cost. Some related work in the current literature can be found under genomic and phenomic prediction.

We are looking for a candidate who is familiar with ODE’s and PDE’s, including numerical methods, as well as statistical methods and machine-learning. For further details and to apply, follow the link: TU Delft PhD application form.

The deadline is November 18.

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Neil Budko

Numerical Analysis, DIAM
Delft University of Technology
Mekelweg 4, 2628 CD, Delft
Netherlands