Understanding Vulture’s Behaviour using Machine Learning​

Summary

​​It is well known that human activity disrupts the movement of animals, leading to significant threats to the conservation of emblematic species and biodiversity. In this regard, understanding the ecological details that determine animals' movement is essential for establishing effective conservation measures. One of the primary motivations for an animal's movement is the acquisition of food. Therefore, being able to determine where and when a species will seek food can be crucial in minimizing the risks that our activities pose to their conservation. Furthermore, understanding the movement networks of animal species increases our ability to mitigate the risks posed by emerging diseases such as avian influenza or the West Nile virus, both for animal and human health.
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​The proposed research aims to utilize advanced machine learning techniques to understand the intricate behaviours and movements of vultures in the south of Spain, focusing on their feeding, foraging, nesting patterns, and responses to potential threats. This project will address key questions related to movement patterns, migration routes, age-dependent behaviours, and habitat preferences. 
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​To facilitate thes tasks, we have access to a number of extensive datasets provided by the Estacion Biologica de Doñana (Spain). These datasets will enable us to conduct in-depth analysis and gain a deeper understanding of vulture behaviours in this region.
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​As such, the project will concentrate on the following research questions:
​Can machine learning assist in predicting vulture feeding and foraging locations, nesting sites, and identifying potential threats? 
​How can machine learning algorithms be used to analyse vulture movement patterns and migration routes using GPS data in Spain? 
​How does vulture age impact their movement patterns and foraging behaviour, as observed through GPS data? 
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​This PhD proposal has the potential to advance our knowledge of vulture behaviour patterns and conservation, the project aims to identify the patterns of vulture feeding, foraging, migration, and danger response. State-of-the-art machine learning algorithms will be utilised to analyse GPS and accelerometer data together with other features collected from the Estacion Biologica de Doñana. The insights and outputs generated from this PhD proposal will contribute to the overall understanding of vulture movement patterns, disease prevention and protection, as well as helping to ensure the survival of these important species. ​

The School of Computing at Ulster University holds Athena Swan Bronze Award since 2016 and is committed to promote and advance gender equality in Higher Education. We particularly welcome female applicants, as they are under-represented within the School.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Sound understanding of subject area as evidenced by a comprehensive research proposal
• A demonstrable interest in the research area associated with the studentship

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.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)
• Vice Chancellor's Research Scholarship (VCRS)

Our fully funded PhD scholarships will cover tuition fees and provide a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of £900 per annum is also available.

These scholarships, funded via the Department for the Economy (DfE) and the Vice Chancellor’s Research Scholarships (VCRS), are open to applicants worldwide, regardless of residency or domicile.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies.