In plants there is a commonly observed trade-off between growth and stress tolerance. Therefore, a highly efficient strategy to survive adverse periods is to stop growing. Regarding flooding stress, it turns out that this is restricted to species from flood prone areas, only these have mastered the art of doing nothing, and are extremely flood tolerant as a result. An important aspect of stopping to grow is to ignore signals that indicate availability of resources (e.g. sugar/energy signalling) and to shut of signals that stimulate growth (hormone signals). At the same time plants have signals that indicate stress, for flooding the gaseous hormone ethylene is important here. 

This PhD project will dissect how distinct components of doing nothing (cell division, cell expansion, deposition of sugars into cell walls, maintenance costs), are regulated by the complex combination of resource, growth and stress signals. To do this end the project can make use of the contrasting behaviour between the model species Arabidopsis thaliana, that is bad at doing nothing when flooded, with the closely related Rorippa sylvestris, which excels at doing nothing when flooded. The project explores the hierarchy between resource, growth, and stress signals on control of growth cessation, builds regulatory networks using genome-scale technologies, and ultimately tests and validates hypotheses with gene-technologies.

Academically, we aim to provide novel insights into how robust and highly redundant growth regulatory networks can evolve, but also how they function to have dynamic growth rates and maintain energy balances. From a human utilitarian perspective, this proposal aims to contribute to crop improvement required to deal with an increased frequency of floods, droughts and other stresses caused by climate change.

(more info: h.van.veen@rug.nl)

Continual learning is a key aspect of intelligence. The ability to accumulate knowledge by incrementally learning from one’s experiences is an important skill for any agent, natural or artificial, that operates in a changing world. While humans are good continual learners, many AI algorithms—in particular deep neural networks—are not.

We are looking for a highly motivated PhD candidate to study the topic of continual learning, with the aim of developing a deeper conceptual and theoretical understanding of its computational principles. For this PhD position, the intention is to approach continual learning mainly from a deep learning perspective. Depending on the interests of the selected candidate, there is also the opportunity to be involved in (international) collaborations investigating continual learning in the brain.

Nowadays, most low-grade organic waste is usually incinerated or converted into low-value products. This results in the loss of valuable raw materials, contributes to greenhouse gas emissions, environmental pollution, and is simply inefficient from any point of view. Within the large consortium ABEL (Ab-initio Biowaste Loop) funded by the Dutch Research Council, we have a PhD student position focused on developing innovative technologies for upcycling this waste into high-performance advanced materials. In collaboration with a post-doctoral researcher, the PhD student will explore how bio-polymer mono-materials made from organic waste can be used to create advanced materials with extended mechanical and/or dynamic functionalities. Based on a known catalytic process, the PhD student will develop numerical models to reproduce the mechanics of bio-based polymers and then apply the principles of metamaterials to activate application-beneficial functionalities, which will make them attractive for industrial needs and, particularly, construction applications.

For the National Growth Fund (NGF) project “Groeien met Groen Staal” (GGS), a PhD position for the period of 4 years is available in the context of modelling green steel mechanics from the atomic scale. In this project, the University of Groningen will investigate atomic-scale mechanism of martensitic, bainitic and ferritic atomic-scale interface motion for multi-component alloy systems that are relevant for the new green steels compositions, including impurities and tramp elements. These models should inform higher-scale (microstructural) models of microstructure formation for green steels.