However, the lack of consistent long-term carbon flux and in situ soil moisture data has severely limited our ability to identify the mechanisms responsible for the recent reduced carbon sink strength. A variety of mechanisms have been proposed to explain the reduced net carbon sink of northern ecosystems with increased air temperature, including water stress on vegetation and increased respiration over recent decades. Long-term atmospheric CO 2 concentration records have suggested a reduction in the positive effect of warming on high-latitude carbon uptake since the 1990s. Oechel Daugherty Water for Food Global Institute: Faculty Publications Humphreys, Oliver Sonnentag, Gesa Meyer, Gabriel H. Miller, Josh Hashemi, Lars Kutzbach, David Holl, Julia Boike, Christian Wille, Torsten Sachs, Aram Kalhori, Elyn R. Christensen, Mikhail Mastepanov, Efrén López-Blanco, Albertus J. Kimball, Martin Heimann, Mathias Göckede, Martijn Pallandt, Torben R. Lafleur, Koen Hufkens, Beniamino Gioli, Barbara Bailey, George Burba, Eugénie S. If you are interested in this project and would like to learn more about the research you will be undertaking, please use the contact details on this page.Pan-Arctic Soil Moisture Control On Tundra Carbon Sequestration And Plant Productivity, Donatella Zona, Peter M. How to applyĪll students can apply using the button below, following the Admissions Statement (PDF, 188kB).īefore applying, we recommend getting in touch with the project's supervisors. Microbiology experience is beneficial though not critical as training will be provided where necessary. You will have a broad interest in data science and artificial intelligence, and the application of these techniques to real-world problems. You will be based in the research group within the School of Geographical Sciences and be integrated into the Quantitative Spatial Science Lab within the same school. You will receive training in microbiology, state-of-the-art imaging techniques, convolutional neural network design, implementation and optimization, as well as several transferable skills in data handling and analysis, academic writing, and presentation. Whilst the overarching aims of the project are established, there is significant flexibility to allow the successful candidate to drive the project more toward the ecological or artificial intelligence disciplines depending on their preference. The successful candidate will work under the supervision of microbiologist Dr Chris Williamson, data scientist Dr Levi Wolf and in collaboration with a leading UK water company to produce robust CNNs capable of detecting, identifying and enumerating harmful microbial communities that dominate UK freshwater systems. Building on the recent success of the in the application of CNNs for the detection and identification of microbial communities from imaged lab and field samples, this project will expand and explore the capacity for CNNs to be used in real-time environmental monitoring of UK freshwater systems. Over the recent past, Artificial Intelligence has significantly reduced the gap between the capabilities of humans and machines, with deep learning convolutional neural networks (CNNs) allowing for rapid object detection and identification from image datasets. Traditional techniques for their identification and enumeration involves time-consuming microscopic analysis of water samples undertaken by highly trained individuals, and represents a significant bottleneck in current monitoring capabilities. Enteroccocus bacteria, harmful cyanobacterial blooms or waterborne protozoan parasites. The requirement for environmental monitoring through observation of key physical, chemical and biological properties is thus greater than ever, particularly for freshwater sources utilised for human consumption.īiological monitoring of freshwater resources involves regular characterization of microbial assemblages that pose potential human health risks, e.g. As the world’s population continues to increase, industrial development and agricultural practices continue to expand, as does their associated pollution. Environmental monitoring is critical to the protection of human health and the environment.
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