Remote sensing and DEEP learning for early warning of WATER hazards (DeepWater), Computer Science – MPhil/PhD (Funded) Ref: 2975

About the award

The University of Exeter’s College of Engineering, Mathematics and Physical Sciences, in partnership with Plymouth Marine Laboratory is inviting applications for a fully-funded PhD studentship to commence in January 2018 or as soon as possible thereafter.  For eligible students the studentship will cover UK/EU tuition fees plus an annual tax-free stipend of at least £14,553 for 3.5 years full-time, or pro rata for part-time study.  The student would be based in the Computer Science department in the College of Engineering, Mathematics and Physical Sciences at the Streatham Campus in Exeter and work in PML for data processing.

Location:

Computer Science, Streatham, Exeter


Academic Supervisors:
Dr Chunbo Luo, University of Exeter
Dr Stefan Simis, Plymouth Marine Laboratory
Dr Shubha Sathyendranath, Plymouth Marine Laboratory
Professor Geyong Min, University of Exeter
Professor Edward Keedwell, University of Exeter


Project Description:


Background
Current satellite sensors allow global monitoring of water resources at an unprecedented optical and spatial resolution, paving the way for operational monitoring of potential hazards from space. Of particular interest is the potential value of satellite observations during and following episodic events such as heavy wind and rain, which may lead to harmful algal blooms, sewage overflow and poor visibility.


Project Aims and Methods
The overarching objectives of this project are to develop image segmentation and object-based satellite image processing techniques for cases where water quality issues are evident. A successful monitoring solution would provide hazard warning information to the affected companies and end users.


The first such case concerns mapping river plumes and their ‘sphere of influence’. Tracing river plumes from their source to the furthest extent, visible as an optical and/or radar signature, directly informs commercial (e.g. aquaculture) and recreational (e.g. diving, fishing, surfing) use of potential risks in the event of strong storm runoff.


The second case focuses on mapping of dynamic features such as potentially harmful algal and cyanobacterial blooms, and relatively stable features such as shallow areas (bottom visibility), and floating vegetation, in coastal and inland water systems. Object oriented mapping would classify these optically dominant structures and create a novel approach to spatial binning on satellite imagery.


The project will seek to tackle the challenge by exploiting the new generation of high resolution satellite imagery (Sentinel-1 (radar), Sentinel-2 (optical), and Landsat optical missions) and numerous advanced computing techniques which have not yet been applied in remote sensing of water bodies, e.g. deep learning, sub-pixel level endmember extraction methods, local parallelisation and distributed processing techniques etc.


This award provides annual funding to cover UK/EU tuition fees and a tax-free stipend.  For students who pay UK/EU tuition fees the award will cover the tuition fees in full, plus at least £14,553 per year tax-free stipend. Students who pay international fees are entitled to apply, but must note that the studentship will only cover part international fees, and no stipend.
The studentship will be awarded on the basis of merit for 3.5 years of full-time study to commence in January 2018 and is subject to confirmation of funding.   


Entry requirements:
Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology. The project will require working experience on earth observation data and large dataset analysis. Experience in scientific writing is desirable. 


If English is not your first language you will need to have achieved at least 6.0 in IELTS and no less than 6.0 in any section by the start of the project.  Alternative tests may be acceptable (see http://www.exeter.ac.uk/postgraduate/apply/english/).

How to apply

In the application process you will be asked to upload several documents.  Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”. 
• CV
• Letter of application (outlining your academic interests, prior research experience and reasons for wishing to undertake the project).
• Research proposal
• Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)
• Two references from referees familiar with your academic work. If your referees prefer, they can email the reference direct to emps-pgr-ad@exeter.ac.uk quoting the studentship reference number.
• If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English


The closing date for applications is midnight on 24th November 2017. Interviews will be held on the University of Exeter Streatham Campus or via teleconference the week commencing 4 December 2017.


If you have any general enquiries about the application process, please email emps-pgr-ad@exeter.ac.uk or phone +44 (0)1392 722730.  Project-specific queries should be directed to the main supervisor.

Summary

Application deadline:24th November 2017
Number of awards:1
Value:£14,553 for 3.5 years
Duration of award:per year
Contact: Postgraduate Research Office emps-pgr-ad@exeter.ac.uk