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2018 NPIF

Long distance drone tracking of key pollinators in agricultural and natural landscapes

Long distance drone tracking of key pollinators in agricultural and natural landscapes

Many plant species, including numerous agricultural ones, depend on pollinator services; yet agricultural intensification and urbanisation have caused habitat loss and fragmentation, leading to substantial declines of some pollinators. Any forecasts, risk assessments and remedies thus hinge crucially on understanding how pollinators use space; however, most studies of pollinator spatial movements have taken place over […]

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Machine learning models of organic soil contaminant bioavailability for the sustainable redevelopment of post-industrial brownfields

Machine learning models of organic soil contaminant bioavailability for the sustainable redevelopment of post-industrial brownfields

This CASE PhD studentship will use a combination of organic geochemistry and data-driven modelling to elucidate the factors responsible for the skin absorption of hazardous organic chemicals in contaminated soils. With increasing pressure to build on brownfield sites there is an urgent need to fully understand the factors responsible for the release of organic contaminants […]

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Species4Services – Which Species and Traits Best Indicate Ecosystem Services?

Species4Services – Which Species and Traits Best Indicate Ecosystem Services?

*******Application deadline – Friday 29th June 2018******* Background: Ecosystem services (ES; the goods humans get from nature) such as crop production, carbon capture and livestock grazing are produced by complex interactions among biological species, human activities and the abiotic environment. Primary data characterising ES are rare and the biological component is poorly understood. Thus, ES […]

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Next generation landscape mapping: machine learning and big data methods for exploiting earth observation data

Next generation landscape mapping: machine learning and big data methods for exploiting earth observation data

This is an exciting opportunity to explore machine learning and big data methods, in combination with earth observation data, to extract information on land cover, landscape features and habitat condition. It will use a range of remote sensing data sets, including LIDAR, aerial photography, high resolution satellite data and satellite time-series. Land cover is a […]

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Optimal Resilience in Distribution Networks

All enquiries to Dr Andrew Jarvis (a.jarvis@lancs.ac.uk)

In both nature and society, distribution networks are fundamental, facilitating the exchange of materials, energy and information. As systems evolve, these networks become complex leading to fragile systems at significant risk of failure. Rail networks are a classic example of this, where timetable pressures amplify the effects of mechanical failure, spikes in demand and adverse […]

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