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  Dr Olaf Marxen  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

The project will combine cutting edge robotic flight experiments with numerical simulations and machine learning techniques to investigate the ways in which flying techniques are learned in nature.

Project Description

Despite recent advances in sensing, actuation and computation, robots cannot come close to matching the agility of natural flyers. Newborn birds and bats outstrip even the most advanced robots within only a few attempts at flight. How does life learn to move in the unsteadiness and chaos that characterise outdoor flight? How does body shape and structure interact with cognition and learning?

The project will combine cutting edge robotic flight experiments with numerical simulations and machine learning techniques to investigate the ways in which flying techniques are learned. Ideally, these experiments will be matched with observations of bird flight using photogrammetry to measure the aerodynamics of live animals. Ultimately, the knowledge generated will feed into new approaches to the design of unmanned aircraft which allow robust and adaptable flight and open new opportunities to deploy aerial robots in the modern world. Insight into the learning process will also help train future pilots.

The PhD research activity will include the development of biomimetic wings, to be used with an existing flight experiment, using a robotic catapult to produce large volumes of flight data in an indoor flight arena. The design of the robotic experiment will be informed by the observation of live bird flight. The resulting data will be used to support advanced numerical modelling to be able to predict and control flight. The numerical modelling approach will be driven by machine-learning techniques and artificial intelligence, which will have been trained for increasingly complex situations.

The student will be a member of the Centre Aerodynamics & Environmental Flow Research in the Department of Mechanical Engineering Sciences (MES) at the University of Surrey. Work on machine learning will be carried out in collaboration with the School of Psychology at the University of Surrey. The members of the research team have an excellent collaboration network with leading research groups in the UK and internationally.

Studentship group name

Surrey Institute for People-Centred AI


School of Mechanical Engineering Sciences

Research group(s)

Centre for Aerodynamics and Environmental Flow

How to Apply

Applications should be submitted via the Aerodynamic and Environmental Flow PhD programme page. In place of a research proposal you should upload a document stating the title of the projects (up to 2) that you wish to apply for and the name(s) of the relevant supervisor. You must upload your full CV and any transcripts of previous academic qualifications. You should enter ’Faculty Funded Competition’ under funding type.


The studentship will provide a stipend at UKRI rates (currently £17,668 for 2022/23) and tuition fees for 3.5 years. An additional bursary of £1700 per annum for the duration of the studentship will be offered to exceptional candidates.


Siddall, Robert, et al. "Between Sea and Sky: Aerial Aquatic Locomotion in Miniature Robots." (2022).
Siddall, Robert, et al. "Tails stabilize landing of gliding geckos crashing head-first into tree trunks." Communications biology 4.1 (2021): 1-12.
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