We have two PhD positions available to start in October 2023, funded by UKRI. Both of these projects are part of larger PhD training programmes- so you apply to the programme and then specify these projects within the application. You can apply for both if you like, but applicants are encouraged to contact Matt in the first instance.
Utilizing Novel Imaging Methods to Resolve Redox Stress Responses Induced by Climate Change, with Prof. Ian Dodd (Lancaster University)
If we are to understand how plants respond to the effects of climate change (including drought and high temperature stress) we need to measure both the immediate changes in metabolism inflicted by stress and the consequences of these changes. Immediate consequences of metabolic stress include production of Reactive Oxygen Species (ROS), in part because of impaired photosynthetic electron transport. ROS have additionally been co-opted as signaling molecules, thereby allowing the integration of metabolic signals into the regulation of nuclear gene expression.
This PhD project will utilize a novel combination of bioluminescent and fluorescent molecular probes to monitor the initial molecular consequences of stress in vivo and reveal how these changes are communicated between shoots and roots. The successful applicant will obtain expertise in cutting-edge imaging methods and apply genetics and molecular biology techniques to understand the biological relevance of these changes. There will also be an opportunity to analyze high throughput sequencing data and gain experience in physiological measurements of plant water status and gas exchange. These skills are highly sought after by industry employers that seek to understand the mechanisms by which bespoke products (e.g. biostimulants) affect plant performance.
You can apply
here or you can
contact Matt for additional information.
Exploiting Novel Imaging to Optimise Crop Yield in Controlled Environment Agriculture, with Dr Qammer Abbasi (University of Glasgow)
Intensive agriculture is essential to feed increasing populations, yet requires large amounts of pesticide, fertiliser, and water to maintain productivity. One solution to mitigate these issues is the adoption of Controlled Environment Agriculture (CEA). The self-contained operation of these facilities offers the potential to recycle agricultural inputs, as well as sheltering crops from the effects of climate change.
Growing crops in CEA systems is now commercially viable, although the associated energy demands limit the choice of crops. One way to minimise the energy footprint of CEA is to vary lighting in parallel with the availability of renewable energy. In order to maximise plant growth with varied lighting we need to understand how plants adapt to these changing conditions. This project will use a combination of novel imaging methods to remotely assess plant performance. Machine learning algorithms will be exploited to predict final yield. These advances will enable farmers to adjust lighting regimes to maximise yield and energy efficiency in CEA systems.
You can apply
here or you can
contact Matt for additional information.