Funding

Funded PhD Project (UK and EU students only)

Project code

COMP5840521

Department 

School of Computing

Start date

October 2021

Closing date

4 May 21 

(12pm GMT)

Applications are invited for a fully-funded three year PhD to commence in October 2021. 

The PhD will be based in the School of Computing and will be supervised by Dr Jiacheng Tan.

Candidates applying for this project may be eligible to compete for one of a small number of bursaries available; these cover tuition fees at the UK rate for three years and a stipend in line with the UKRI rate (£15,609 for 2021/22). Bursary recipients will also receive a £1,500 p.a. for project costs/consumables. 

The work on this project could involve

  • Modelling of task states, behaviour of swarm members and impact of adversaries
  • Machine learning of task manoeuvre and co-operative behaviour
  • Application of theories and methods of swarm intelligence and multi-agent systems

The project is the spin-off of EU H2020 project NEANIUS. It studies the core issues in developing unmanned aerial vehicle (UAV) swarms that work cooperatively to achieve goals that are difficult or impossible for individual UAVs to do. Recent years have witnessed the success in UAV applications, ranging from telemetry, precision agriculture and surveillance in civil space to reconnaissance and precision-guided strikes in battlespace. However, an area that is envisaged to have far greater potential is the applications of UVA swarms. A swarm of co-operative UVAs can seamlessly cover large inaccessible areas to support massive scale operations such as geological surveys, environment monitoring, and rescue operations in natural disasters. Defence research in many countries is also investing in the work of intelligent swarms in a bid to build swarm UAVs that are capable of working together to overwhelm adversaries or "swarm squadrons'' that can take offensive and defensive actions by themselves in the battlefields. The big challenge in developing such UAV swarms is to equip the UVAs with the ability of self-organisation and task intelligence that would enable them to automatically assess and predict the task states, recognise gaps in task execution as task states and conditions evolve, and pre-emptively devise action plans in situ without the intervention from human operators. Progresses have been made in enabling a UAV swarm to exhibit emergent intelligent behaviour such as forming coherent flock formations and collectively reacting to external perturbations. However, in such swarms the UAVs remain behaving individually and no task-driven co-operation is involved. Based on our work in intelligent robots, this project will approach the problem of intelligent UAV swarms via learning of task manoeuvre and cooperative behaviour in the state space of local and global task variables, states of local swarm members, and adversary factors.

Entry requirements

You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Ideally, you should have a degree in the disciplines of artificial intelligence, computing, information technology or engineering. Experience in machine learning, UAV control and robotics would be desirable.

How to apply

We’d encourage you to contact Dr Jiacheng Tan  (jiacheng.tan@port.ac.uk) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. 

Our ‘How to Apply’ page offers further guidance on the PhD application process. If you want to be considered for this funded PhD opportunity you must quote project code COMP5840521  when applying.

 

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