This scholarship is funded by UK Research and Innovation (UKRI).
Start date: October 2021
The UK Research and Innovation (UKRI) Centre for Doctoral Training (CDT) in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society.
Our doctoral training programme is constructed around three research themes:
- T1: data from large science facilities (particle physics, astronomy, cosmology)
- T2: biological, health and clinical sciences (medical imaging, electronic health records, bioinformatics)
- T3: novel mathematical, physical, and computer science approaches (data, hardware, software, algorithms)
Supervisors:
- First supervisor: Dr Noemi Picco (Mathematics, Swansea University)
- Second supervisor: Dr Farzad Fathi Zadeh (Computer Science, Swansea University)
- Potential supervisor/Collaborators: Dr Thomas Woolley (Cardiff University) and Professor Fiona Mathews
Department/Institution: Mathematics / Swansea University
Research theme: T3: novel mathematical, physical and computer science approaches
Project description:
Wind turbines are an important threat to bats. We currently do not understand why bats collide with turbine blades, and the rates of collisions seem high relative to the amount of observed activity. Recent studies found that bat activity appears to be higher at turbines than at control sites, and there is limited evidence from the USA using thermal imaging that appears to show repeated approaches of bats to the blades. These pieces of evidence suggest that there may be some attraction to the structures. Earlier this year, a paper was published describing a specific type of social call, produced by common pipistrelle bats, that is associated with chasing behaviour (Götze, Denzinger et al. 2020). We will test the hypothesis that these social calls are more common at wind turbines than at control sites, suggesting that bats may either be chasing other individuals, or indeed the turbine blades.
The student will work on a large dataset of sound files available from >50 wind farms across Britain. Matched control sites are available for a subset (c. 20) of these. Initially, sound analysis can be carried out using methods specifically developed for nonlinear and non-stationary data (Huang et al. 1998). The project would involve adoption of machine learning approaches to data analysis, developing automated pattern recognition workflows to identify bats species and patterns relative to the nature of the call.
Eligibility
The typical academic requirement is a minimum of a 2:1 undergraduate degree in biological and health sciences; mathematics and computer science; physics and astronomy or a relevant discipline.
Candidates should be interested in AI and big data challenges, and in (at least) one of the three research themes. You should have an aptitude and ability in computational thinking and methods (as evidenced by a degree in physics and astronomy, medical science, computer science, or mathematics, for instance) including the ability to write software (or willingness to learn it).
This scholarship is open to UK and international candidates (including EU and EEA).
How to apply
To apply please visit our website.
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