Dr Chris Hassall
Dr E.J.. Duncan
Dr R Neely
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
In the 21st Century, Big Data is an increasingly important and dominant component of the scientific landscape. The large quantities, velocities and diversities of data that are collected within different fields often have the potential to yield considerable insights both within those fields as well as in other areas of research. However, unlocking the potential of those Big Data streams requires interdisciplinary working between computationally skilled researchers from complementary fields. This interdisciplinary PhD project, which links atmospheric science with biology, is designed to assess to what extent measurements from a range of tools, instruments, and techniques within atmospheric physics can be used to generate useful biological information that can be applied to solve contemporary ecological problems. The project will involve (and provide training in) a range of techniques from physics, ecology and meteorology.
Using these techniques the student will use actively and passively collected data from different atmospheric and meteorological sources to develop ways of extracting biological information. These methods will then be used to analyse real-world data sets to investigate and visualise spatio-temporal patterns in the biological data to test old hypotheses with this new approach. We will target hypotheses that have been simply intractable to contemporary methods of biological study due to the scale of data that has been required. The student will be able to explore not only spatio-temporal analysis, geostatistics, and macroecology, but also data visualisation and communication of outputs to stakeholders.
This PhD project has an exciting interdisciplinary focus that will produce considerable impact. Key external partners both within the UK (Natural England, Centre for Ecology and Hydrology, BugLife) and abroad (in the US and South Africa) who will be involved in discussion and guiding the project. The project would benefit from a student with strong quantitative skills in data analysis (including training specifically in data science, if possible), an interest in harnessing the power of large datasets for novel purposes, and an interest in solving real world environmental problems.
Project is eligible for funding under the FBS Faculty Studentships scheme. Successful candidates will receive a PhD studentship for 4 years, covering fees at UK/EU level and stipend at research council level (£14,777 for 2018-19).
Candidates should have, or be expecting, a 2.1 or above at undergraduate level in a relevant field. If English is not your first language, you will also be required to meet our language entry requirements. The PhD is to start in Oct 2018.
Please apply online here: https://studentservices.leeds.ac.uk/pls/banprod/bwskalog_uol.P_DispLoginNon When you apply, please include the project title and supervisor name, and upload a CV and transcripts.
Baude M, Kunin WE, et al. (2016) Historical nectar assessment reveals the fall and rise of floral resources in Britain. Nature, 530, 85.
Carvalheiro LG, … Kunin WE (2014) The potential for indirect effects between co-flowering plants via shared pollinators depends on resource abundance, accessibility and relatedness. Ecology Letters, 17, 1389-1399.
Hassall, C., et al. (2017) Phenological shifts in hoverflies (Diptera: Syrphidae): linking measurement and mechanism, Ecography, 40: 853–863.
Ziv, G, Hassall, C, et al. (in press) A bird’s eye view over ecosystem services in Natura 2000 sites across Europe, Ecosystem Services.
Goerke, M, …Neely, R, et al. (2017) Characterizing ice particles using two-dimensional reflections of a lidar beam, Applied Optics.
Stillwell, R., Neely, R, et al. (2017): Improved Cloud Phase Determination of Low Level Liquid and Mixed Phase Clouds by Enhanced Polarimetric Lidar, Atmos. Meas. Tech. Discuss.
How good is research at University of Leeds in Biological Sciences?
FTE Category A staff submitted: 60.90
Research output data provided by the Research Excellence Framework (REF)
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