Funded PhD Opportunity

Gaining a better understanding of our Planet through Deep Learning-based Data Analytics

Subject: Computer Science and Informatics


Summary

Earth observation satellite imagery and electromagnetic signals are widely used for capturing land usage, deformation and climate change over time on a regional and global scale. The aim of the project is to help observe and better understand the evolution of the land environment and inform experts so greater resilience to geohazards can be produced.  When combined, the imagery and signals contain the degree of detail to allow environmental changes to be monitored. A series of studies have been conducted on change detection from satellite data at Ulster University, including the development of data analytics algorithms for detecting seismic anomalies from the satellite and electromagnetic data. Additionally, environmental factors have been extracted from satellite images for understanding vector-borne diseases.

Currently, with the advance of sensor technology, spatial, temporal and spectral resolutions of satellite imagery and electromagnetic signal have been significantly advanced to allow even finer grain investigations, but this generates greater volumes of data that needs to be analysed and classified. The project will develop and integrate detection algorithms on multi-source, multi-resolution and multi-spectral satellite data at various levels of detail to monitor change detection.

The research will investigate the data analytic techniques that are underpinned by machine learning with a particular focus on using deep learning technologies. The proposed project will develop algorithms, which will be used to detect abnormalities in the environment found in the data, such as, regions prone to air pollution or opportunities to improve sustainable development of energy.

The project will involve:

1) the use signal analysis tools to extract pixel and object-based features from satellite imagery to identify relevant geographical features;

2) combine the satellite imagery and wavelet features detected to develop techniques using the state of the art machine (deep structured, such as TensorFlow) learning approaches; and

3) evaluate the developed techniques for a selected domain, such as, studying earthquakes, energy, vector-borne diseases, monitoring air pollution or pest control in agriculture to allow experts to better understand our Planet.


Essential criteria

  • To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
  • Sound understanding of subject area as evidenced by a comprehensive research proposal

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • Publications record appropriate to career stage
  • A comprehensive and articulate personal statement
  • Applicants will be shortlisted if they have an average of 75% or greater in a first (honours) degree (or a GPA of 8.75/10). For applicants with a first degree average in the range of 70% to 74% (GPA 3.3): If they are undertaking an Masters, then the average of their first degree marks and their Masters marks will be used for shortlisting.

Funding

    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:

    Vice Chancellors Research Studentship (VCRS)

    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 support grant (RTSG) allocation to help support the PhD researcher.

    Vice-Chancellor’s Research Bursary (VCRB)

    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 support grant (RTSG) allocation to help support the PhD researcher.

    Vice-Chancellor’s Research Fees Bursary (VCRFB)

    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 support grant (RTSG) allocation to help support the PhD researcher.

    Department for the Economy (DFE)

    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 support 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


Other information


The Doctoral College at Ulster University


Reviews

Profile picture of Adrian Johnston

As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day

Adrian Johnston - PhD in Informatics

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Key dates

Submission deadline
Friday 7 February 2020

Interview Date
Late March 2020


Applying

Apply Online  


Campus

Jordanstown campus

Jordanstown campus
The largest of Ulster's campuses


Contact supervisor

Dr Yaxin Bi


Other supervisors

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