This opportunity is now closed.
Funded PhD Opportunity
The Arctic is undergoing rapid environmental change that is unprecedented in modern times. ‘Arctic amplification’ is rapidly destabilising Ice sheets and glaciers which are known to influence global sea levels. Currently, there is concern regarding the future stability of the cryosphere in a warming world and if ice sheets and glaciers continue to melt, global sea levels will potentially rise by over 90m. Given that most of the world’s population lives within several kilometres of the coast, it is critical that we develop a better understanding of how ice sheets and glaciers are responding to climatic warming so that governments can develop mitigation strategies for future generations.
Projections of glacier change in the Arctic rely on observations of recent glacier change and an understanding of the processes that are driving these changes. Currently, there is a large imbalance across the Arctic, with most studies focussing on the Greenland Ice Sheet and Canadian Arctic Archipelago and with less research for example on Svalbard and the Russian High Arctic.
This means that our understanding of past changes is incomplete and that there are large uncertainties for some regions when attempting to predict future change. To understand what is happening we need robust monitoring tools that can be applied Pan-Arctic and using new satellite remote sensing datasets such as the Copernicus Earth observation (EO) programme provides an unprecedented opportunity to develop new methods for mapping changes to glacial systems in this region.
This interdisciplinary PhD project involving the School of Geography and Environmental Sciences and Intelligent Systems Research Centre will build on the expertise developed in both research centres to focus on change detection of Arctic glaciers using remote sensing data and novel machine learning techniques.
The rapid increase in computing power has enabled the use of powerful machine learning algorithms on large datasets. In particular, recent breakthroughs in computer vision methods and deep learning models for image classification and object detection now make it possible to automatically obtain a much more accurate representation of the composition of the environment than could be previously achieved. It is only recently that advances in deep learning methods have permitted the analysis of satellite imagery and as such, land use classification is still at an early stage but this approach shows great potential for identifying landscape changes.
In this project, low-level (pixel) image processing approaches and object-based approaches will be used in conjunction with deep learning for temporal analysis of satellite imagery for change detection in glaciated landscapes. Algorithms will be developed to automatically learn regional representations using a deep neural network in a data-driven fashion. Based on these highly discriminative representations, changes will be determined and predicted using low-level and object based labelling of the candidate images. Innovative, user-friendly tools will be developed to map the impacts of such climate changes which are required to inform and engage communities.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:
Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.
Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £7,500 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.
Fees only award (PhD fees + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 15,009 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.
Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies
Completing the MRes provided me with a lot of different skills, particularly in research methods and lab skills.
Michelle Clements Clements - MRes - Life and Health SciencesWatch Video
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Carin Cornwall - PhD Environmental SciencesWatch Video
Monday 18 February 2019
w/c 18 March 2019
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When applying for this PhD opportunity please quote reference number: