Geocomputation U-Flyte research - Funded PhD Projects

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    Maynooth University
    Formal sciences
    Professions and applied sciences
    Social sciences
    First Stage Researcher (R1) (Up to the point of PhD)


U-Flyte Strategic Partnership

Funded by Science Foundation Ireland and involving collaboration with Industry partners including Airbus, Irelandia Aviation and Intel, U-Flyte is a R&D partnership, established to tackle the current global logjam impeding the wider development of drone operation and rollout of commercial services. The research work-plan is based around a series of inter-connected work-packages that deal with the development of novel airspace modelling tools and drone traffic management systems – also known as U-Space and Unmanned Traffic Management (UTM) systems. Advanced flight-testing is carried out at Waterford Airport and selected mobile locations across Ireland. Drone services including mapping, monitoring and logistics. Specialist aerial support services will also be developed and tested using real-world, end-use case scenarios. U-Flyte comprises Geospatial Scientists, Software Engineers, Mathematicians, Domain Specialists and Drone Operations personnel, working with the latest Vertical Take-off and Landing (VTOL) and hybrid drones, sensors (Optical, LiDAR, Navigation, Radar) and cloud computing resources. At the heart of U-Flyte is advanced computational expertise including autonomous navigation, Machine Learning and Geospatial Analytics all dedicated to developing the next generation of Beyond Visual Line of Sight (BVLOS) autonomous drone services.

U-Flyte now wishes to recruit PhD candidates to join this aerial robotics team based within the National Centre for Geocomputation (NCG), an established research centre at Maynooth University. We would especially like to hear from candidates with good primary degrees in Computing or Mathematics interested in developing careers in Aerial Robotics, Machine Learning, Computer Vision & Geospatial Data Analytics. If you feel you have the right background and want to know more – please get in touch.


PhD Topics

  • 3D Pathfinding & traffic management architectures for large-scale urban drone operations

This project focuses on the creation of new U-Space models & UTM systems designed to handle large numbers of autonomous drones carrying out a variety of data gathering, logistic and robotic activities in urban environments. This PhD will explore various algorithms and models that are required for constructing these new airspaces and enabling optimal traffic routing and overall management.

  • Developing automated risk analysis and modelling tools for drone operations

Drone operation will always result in risk to varying degrees and both static and dynamic sources of risk need to be measured, classified and ultimately understood. Static risk includes vulnerable zones such as school yards, car-parks and exposed recreational parks. Dynamic risk includes weather and indeed other drones in flight. This PhD will deal with developing new methodologies and computational models to record, analyse and model risk as a fundamental input to U-Space/UTM system design.

  • Sensor fusion for innovative Aerial and Ground based Detect and Avoid (DAA) systems

At the heart of this PhD will be the principle that drones can only fly safely,

undertaking BVLOS operations, if all relevant aspects affecting flight safety are known. One key part of this is emerging optical, radar and RF-Sensor technology for detecting, locating and tracking both fixed and dynamic obstacles at low altitudes. This PhD will focus on how these new emerging technologies can be fused and analysed using the latest Machine Learning techniques to allow drones to fly autonomously over cities, towns and farms, along critical infrastructures and coastlines.

  • Adaptable Machine Learning techniques for dynamic aerial scene understanding

Drones are capable of flying for more than an hour, covering tens of kilometres in distance, recording GigaBytes of combined optical and navigation data. Automated classification and measurement of man-made and natural features will become an increasingly important role for data-gathering drones. This PhD will investigate novel Machine learning (ML) tools and methodologies for identifying objects and activities in real-world scenes. These new ML tools will be tested and assessed for a variety of mapping, monitoring, defect inspection and anomaly detection tasks.

What is funded

U-Flyte Doctoral Scholars will receive an annual (tax-free) stipend of €18,000 (paid monthly) and tuition fees over the 4 year PhD duration, will be paid at the EU rate, directly by the project.

Scholars will have a dedicated work-space/I.T. within the NCG’s postgraduate area and join an experienced team operating in an innovative, dynamic environment. 



4 years full-time


 - Candidates would normally be expected to have an overall 2.1 Honours award at Bachelors or Masters level, or equivalent

-  Candidates must be able to meet the minimum English language requirements



The responsibility for the funding offers published on this website, including the funding description, lies entirely with the publishing institutions. The application is handled uniquely by the employer, who is also fully responsible for the recruitment and selection processes.