Graduate Teaching Assistant (GTA) - Bird Flights: Flying Probes of Atmospheric Turbulence

We are pleased to announce a fantastic opportunity for ambitious computer scientists to join our Computer Science Graduate Teaching Assistant (GTA) Programme!

How does it work?

Candidates will study for a four year, full time funded PhD (3 quarters of your time) whilst working and receiving a salary to gain valuable teaching experience (1 quarter of your time). Candidates will receive a salary and stipend package that exceeds the standard UKRI stipend for a full-time PhD.

Home/RoI Students will have their PhD fees waived, International students will receive a fee waiver equivalent to the Home/RoI fee and will be expected to fund the difference between the International fee and the Home/RoI fee. There will be a package of support to enable you to develop a research career in this exciting field.

PhD Topic: Bird Flights: Flying Probes of Atmospheric Turbulence

Vast amounts of animal tracking data are now being collected at global scales. Recent work has shown that the fine-scale movements of birds vary in relation to atmospheric turbulence. Animal-attached sensors could therefore be used to probe atmospheric conditions, providing measurements in locations and scenarios that are inaccessible to traditional meteorological probes. It can also teach us about their physiological state. This project will use a range of methods including causal inference, signal processing and machine learning to investigate whether and how acceleration data from flying birds can be used to characterise changing levels of atmospheric turbulence.

The central goal of the project is to characterise turbulence from animal movement data with the following objectives:

1. Assess the influence of gusts on fine-scale movements of homing pigeons. Data from accelerometers attached to pigeons exposed to experimentally generated gusts in wind tunnel trials are available (varying in magnitude and period). A suitable combination of signal processing and causal inference methods will be designed to examine use of tracking data in predicting the gusts. If successful, methods will be extended to pigeons homing in the wild.
2. Examine the relation between sustained turbulence and fine-scale movements of birds. Data from gannets flying within and outside the wakes of offshore wind farms will be used to study signatures of turbulence in acceleration data. A combination of Computational Fluid Dynamics models with machine learning (e.g. classification) and statistical techniques will be applied to characterise turbulence patterns experienced in wind farm wakes.

You will need to demonstrate you:

• Meet the academic requirements for a PhD offer from the University of Reading.
• Have a good (1st or 2.1) first degree in Computer Science, Statistics, Data Science, Mathematical Science, Meteorology, Physics or closely related subjects.
• Are able to effectively organise your time and prioritise tasks to balance PhD studies with GTA responsibilities.
• Are able to demonstrate scholarship in developing a publication record in your area of specialist expertise and conduct high quality PhD research.

How do I apply?

You must upload a combined CV and Proposal in pdf format (max size 1 MB) and complete the supporting statement.

Closing date: 04/042025

Interview: week commencing 21/04/2025

We look forward to hearing from you!

Contact details for advert

Contact Name Dr Nachiketa Chakraborty

Contact Job Title Lecturer in Computer Science

Contact Email address n.chakraborty@reading.ac.uk 

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