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VC International Scholarship in Semantic Data Integration and Modelling Towards a Forest Observatory (eligibility criteria apply)

  • Full or part time
    Dr C Perera
    Prof O F Rana
  • Application Deadline
    Monday, June 01, 2020
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Poaching is a significant challenge around the world. Anti-poaching efforts are always under-funded and under-resourced. Law enforcement officers cannot keep up with a large number of poachers who are trying to kill animals. Due to limited manpower, they cannot patrol and protect vast areas of land (e.g. sanctuaries).

Proposed Solution (In Brief): In this PhD project, we will combine data gathered by bioscience researchers and environmental scientists to predict where poaching activities may occur in the future. Our data-driven prediction models will identify areas and time frames which have a high likelihood of becoming poaching incidents, thereby enabling law enforcement agencies to deploy their limited resource more effectively by focusing into those areas. This project will focus on the Lower Kinabatangan Wildlife Sanctuary, Sabah, Malaysia, and it is an interdisciplinary collaboration between Cardiff University’s School of Computer Science and the School of Biosciences (and its Danau Girang Field Centre; DGFC). The supervisor panel will comprise both computer science and bioscience researchers.

Context and Problem Scenario: Most of the time, poaching occurs in developing countries where law enforcement is of limited resources and manpower. Whether a developed nation or a developing one, it is not financially sustainable to continuously increase human capital (e.g. wildlife officers) beyond a certain level to patrol the protected areas due to competing demands for resources. Furthermore, as wildlife populations reduce in size due to illegal hunting, poachers increase their pressure to gain access to the ever-smaller populations. Therefore, we need to develop cheaper and scalable solutions to tackle the poaching problem that will help and augment the capabilities of the limited number of wildlife officials to use their time and resources in the most optimum way.

This project aims to develop a Forest Observatory and develop data-driven predictive analytics to predict poaching incidents. Forest Observatory is a Linked Datastore which integrates heterogeneous data. We consider Forest Observatory as an extension to Urban Observatories which aim at gathering real-time urban data across cities. For example, Danau Girang Field Centre (DGFC) in Malaysia has data sets collected by researchers for wildlife species monitoring over the last decade, such as animal collar data, camera traps, satellite imagery, LiDAR and environmental data, with each data set generated using different time frames, durations, geographic areas etc.

The student will tackle the following research question and objectives:
• [Primary] Can we use heterogeneous data gathered by bioscience researchers and environmental scientists to predict where the poaching activities would occur in the future?
• [Secondary] Can we develop a Linked Datastore (that conform to standards in Bioscience research) that semantically integrate and interlink heterogeneous data in order to facilitate data scientists towards developing new apps? (we will demonstrate this by developing an app to predict poaching activities)
o To make forest data available for scientist through open standards (e.g. ontologies) so they can easily acquire data without having to worry about what exact sensors or methods are used to gather them.
o To develop techniques to enable semantic querying and reasoning across heterogeneous data sets.

This project focus on SUSTAINABLE DEVELOPMENT GOAL 15 which aim to Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. This project fits well within Sustainable Places Research Institute at Cardiff University.

Supervisory Team:
Charith Perera (School of Computer Science & Informatics)
Omer Rana (School of Computer Science & Informatics)
Benoit Goossens (School of Biosciences)

RESEARCH ENVIRONMENT
Join our internationally recognised research culture, with exceptional computing resources and expert research leaders. The Doctoral Academy Programme develops research and professional skills, and workshop / conference attendance maximises the wider research context.

VC INTERNATIONAL SCHOLARSHIP FOR RESEARCH EXCELLENCE
Duration: 3 years, subject to satisfactory progress
Start: 1 October 2020.

ACADEMIC CRITERIA
2:1 or above Honours undergraduate or a master’s degree, in computing or related subject. Applicants for whom English is not their first language must demonstrate proficiency by obtaining IELTS at least 6.5 overall, with minimum 6.0 in each component.

HOW TO APPLY
Visit the website to apply for qualification Doctor of Philosophy in Computer Science & Informatics, mode of study Full-time, with start date 1 October 2020. In the research proposal section, specify the project title and supervisors. In the funding section, enter " VC International Scholarship".

Funding Notes

Scholarship covers:
- overseas tuition fees;
- annual stipend (£15,285 for 2020/21);
- research consumables, training, conference travel.
Students earn additional income supporting teaching.

ELIGIBILITY
Applicants must:
- be nationals of (or permanently domiciled in) the world’s Least Developed and Other Low Income Countries based on the DAC list of ODA Recipients 2020. See
View Website
- meet the academic criteria;
- be liable to pay overseas tuition fees;
- not have been awarded another scholarship covering both tuition fees and stipend.
If you have either a tuition-fee only or stipend only award, you are eligible but will not receive double-funding.

How good is research at Cardiff University in Computer Science and Informatics?

FTE Category A staff submitted: 13.73

Research output data provided by the Research Excellence Framework (REF)

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