Self-funded PhD students only

Project code



School of Computing

Start dates

October and February

Closing date

Applications accepted all year round

Applications are invited for a self-funded 3-year PhD.

The PhD will be based in the Faculty of Technology, and will be supervised by Dr Jiacheng Tan, School of Computing and Dr Mel Krokos, School of Creative Technologies.

The work on this project will involve:

  • Deep Learning (CNN-based) for object recognition and affordance detection
  • Object modelling and 3D point cloud segmentation
  • Semantic knowledge modelling

The project pioneers an AI framework that integrates robot learning, scene understanding, task description and knowledge modelling for intelligent robot manipulation. It is in accordance with our research of the EU Horizon 2020 project (NEANIUS 863448) under the theme of integration robots into the European Open Science Cloud for planetary and underwater services, but it pushes the research frontier towards the direction of intelligent manipulation – one of the main challenges of intelligent robot research.

The research outcomes from this project would deepen our understanding of human perceptual-motor mechanisms and bring substantial research opportunities in the coming years in forging the emerging technology of immersive intelligent robots in human life and society. The work will be undertaken in collaboration with Aix-Marseille University, University of Bremen, University of Milan-Bicocca, National and Kapodistrian University of Athens and other European partner institutions.

Intelligent manipulation is the problem of devising strategies and plans for robots to negotiate with objects in the given task contexts. Such robots need to be task and environment aware and be able to devise the optimal manipulation policies in situ. Research has evidenced that intelligent manipulation is a complex AI problem that involves robot learning, knowledge acquisition and modelling, object recognition and scene understanding and more.

Recent research has witnessed progresses in the isolated areas such as affordance learning, grasp planning and manipulation-knowledge modelling. However, there is still no breakthrough in developing a unified AI model representing the complex nature of the problem and reflecting the confluence of the various mechanisms.

This project will study the intrinsic mechanisms and knowledge models underpinning the intelligent manipulation and aims at establishing a framework that integrates robot learning, object modelling, task descriptions, and policy generation within a unified model of manipulation planning. The model will address the issues of task- and context-awared policy making through multimodal learning, knowledge sharing and knowledge-driven task learning.

Fees and funding

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).

2020/2021 entry (for October 2020 and February 2021 entries)

Home/EU/CI full-time students: £4,407 p/a

Home/EU/CI part-time students: £2,204 p/a

International full-time students: £16,400 p/a

International part-time students: £8,200 p/a

PhD by Publication

External candidates £4,407 p/a

Members of staff £1,680 p/a*

2021/2022 entry (for October 2021 and February 2022 entries)

PhD and MPhil

Home/EU/CI full-time students: £4,407 p/a*

Home/EU/CI part-time students: £2,204 p/a* 

International full-time students: £17,600 p/a

International part-time students: £8,800 p/a

All fees are subject to annual increase.

PhD by Publication

External Candidates £4,407 p/a*

Members of Staff £1,720 p/a*

If you are an EU student starting a programme in 2021/22 please visit this page.

*This is the 2020/21 UK Research and Innovation (UKRI) maximum studentship fee; this fee will increase to the 2021/22 UKRI maximum studentship fee when UKRI announces this rate in Spring 2021. 

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

Entry requirements

Entry requirements

You'll need an upper second class honours degree from an internationally recognised university 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 deep learning, computer vision or semantic modelling would be advantageous.

How to apply

We encourage you to contact Dr Jiacheng Tan at 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. An extended statement as to how you might address the proposal would be welcomed.

Our ‘How to Apply’ page offers further guidance on the PhD application process.

If you want to be considered for this self-funded PhD opportunity you must quote project code COMP4550220 when applying.

October start

Apply now

February start

Apply now

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