Find out more about PhD Studentships

This project is fully funded and co-led by Chelton Limited and will be based at the University of Nottingham.

Project application reference GEO23_Chelton

Please note this opportunity is only available to students who qualify for home fees.

GNSS interference and spoofing is now commonly seen throughout the globe and is a significant threat to our widespread use of GNSS.  If not addressed this will limit the future use of GNSS instruments for science, commercial and leisure activities.

Anti Jam is established as is anti spoofing capability within receivers. Chelton wish to explore the options, performance trade-offs and limitations of anti spoofing capabilities within anti jam equipment.

The aim of the PhD is to research spoofing detection and mitigation techniques within an Anti-Jam system as a compliment to or replacement of receiver based spoofing techniques.

Chelton envisage 3 outline areas for evaluation:

Array based techniques

Receiver based detection with AJ based mitigation

Receiver on board the AJ for both detection and mitigation using correlator techniques

The work will include:

Evaluation of the options and the trade offs and limitations for anti spoofing as part of an anti jam system

Investigation of traditional receiver based Anti-spoof detection techniques with an adaptive array in the processing chain. Leading to an understanding of the impact of the adaptive array

Array based spoofer detection and mitigation techniques

Investigate receiver embedded within an anti-jam also considering commercially available receivers

Modelling and simulation techniques

The development of array based algorithms that give simultaneous AJ/AS signal

An architecture to upgrade test environment so that we can prove this out

Spoofer signal intelligence – direction of arrival, geolocation, spoofer type

What satellites and constellations are being spoofed

During the MRes, the successful candidate will investigate the options, what is possible, trade offs and the limitations. Resulting in a best fit solution for years 2-4 understand impact of an array in an anti-spoof processing chain. Access for our array model and our test set and equipment would be provided.

For further information, please contact Dr Paul Blunt. 

This project is co-funded by Mitsui O.S.K. Lines (MOL) and will be based at Newcastle University

Project application reference GEO23_MOL

This research will build on the successful collaboration between two of the highest-impact research groups at Newcastle University ie the Electrical Power and Marine Subsea and Offshore Technology Groups in the School of Engineering. These groups have been successful in securing funding in two of the latest rounds of Clean Maritime Demonstrator calls by government. The project will analyse existing trade routes and gather data to understand current ship and shore-side GHG emissions based on vessel types, trade volumes and usages of energy. We will identify gaps and synergies between energy, geospatial, infrastructure, and logistics data spreading across multiple spatial and temporal scales to establish high-impact green corridors. We will develop and use digital twin technologies to analyse complex energy and logistics eco-systems to support joint decision-making.

Given the UK’s pledge to net-zero and the need now more than ever to diversify our fuel and energy portfolio, particularly in high-emission sectors, this project lies at a critical junction for industry, academia, and government, providing insights and decision-making support tools that can only be realized through a truly inter-disciplinary approach.

During the MRes, we will define and agree zero-corridor scope and map to existing routes, vessels, and ports. Routes and ports studied the ongoing projects mentioned above could be considered for a proof of concept. Define and agree Digital Twin functional specifications and data requirements. Collect and analyse data and scope data requirements for current and future energy/logistics interactions.

Following the MRes, in year 2, the PhD will include initial design and demonstration of fundamental functionality – Design and testing of digital twin using historical data and set values. The digital twin will be implemented on a modular basis, enabling proof-of-concept in a specific route that can be rolled out across multiple corridors. It is anticipated to include:

Historical geospatial data inputs as well as ship energy and physical network data

Network state estimation and validation

Identification of network and infrastructure constraints and parameters

Quantification of the cost of ensuring these constraints and the value in resolving them

In years 3 – 4 we will upgrade Digital Twin with forecasting and real-data input capabilities. Embed technical network and technology constraints, and perform calculations of locational price and volume of flexibility. This will include more detailed models of infrastructure such as round-trip efficiency for energy storage, logistical constraints and other ‘random’ events (eg strike actions at ports), weather and climate considerations.

The successful candidate should have knowledge of programming in Python, Julia, Matlab or other high-level language and knowledge of working with GIS tools along with an understanding of marine and energy systems

for further information, please contact Dr Haris Patsios

This project is fully funded and will be co-led by Ordnance Survey and will be based at Newcastle University

Project application reference GEO23_OS

Ordnance Survey, Great Britain’s National Mapping Agency, helps governments make smarter decisions that ensure our safety and security, aids businesses to gain a location data edge, and assists all of us to experience the benefits of getting outside. Whilst Ordnance Survey has been at the forefront of mapping the 3D world around us since 1791, national-level mapping products have traditionally been limited to what can be best described as 2.5D. However, new data acquisition systems (e.g. road vehicles, remotely piloted airborne systems (RPAS), aeroplanes or satellites) and processing technologies (e.g. machine learning and geo-artificial intelligence (geo-AI)), together with new computing paradigms, including cloud computing, are facilitating the transition of the digital twin representation of the UK to its full three dimensions.

The challenge is in moving from mapping project sites such as city centres to creating a national 3D dataset. Firstly, our primary mapping technology might be aircraft-based which balances access and area coverage with resolution but tree cover and features like high walls and hedges will obscure the view. This means we will need to fuse data from other viewpoints, e.g. ground-based survey methods. The processing workflow needs to be mostly automatic to cope with the big data volumes of national coverage but also needs to be flexible to cope with this variation of input data.

The second challenge is that once we’ve created our national product, we will need to keep it up to date. How do we receive new survey data (again, from a possible variety of sensors) and use it to consistently update the master model? And do this as automatically as possible.

One PhD may not answer all aspects of these challenges but we’re looking for someone who’s interesting in engaging in a multi-faceted data science challenge including big datasets, multiple sensor sources and real-world customer problems.

In the first year of the studentship, as part of the MRes in Geospatial Data Science, the successful candidate will familiarise themselves with different forms of 3D digital model, e.g. point clouds, terrain models, meshes and 3D feature geometry, and use cases, e.g. city energy modelling, urban planning, policing, to understand the drivers for product specifications. The candidate will also review current 3D mapping technologies and suppliers to understand what is available in the market. Review and evaluation of Ordnance Survey 3D production and map revision pipelines, including time spent at Ordnance Survey offices in Southampton. Familiarisation and experimentation with integration of diverse local datasets to explore the art of the possible in 3D level-of-detail digital data production and a project-based research demonstration of targeted multi-source 3D data fusion will also be undertaken.

The PhD programme will commence with the student spending time at Ordnance Survey and other national mapping agencies to better appreciate best practice in 3D data production, as well as socialising their own MRes findings. Proposed methods, which will call on the big data (e.g. spaceborne, airborne, terrestrial, mobile and RPAS-based), machine learning and visualisation skillsets of the student developed through the MRes taught programme, will be tested and deployed, leading to a definition and demonstration of an efficient, updatable and highly-detailed 3D data product for Great Britain.

Good undergraduate or masters degree in geomatics, geography, computing science, civil engineering, mathematics, or related disciplines is essential. Remote sensing expertise, programming skills (Python or C++), practical surveying experience are desirable.

For more information, please contact Professor Jon Mills