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About the studentship

Background

Humanitarian crises are complex situations involving many actors and protection frameworks, all operating in parallel. In the midst of this are people on the move who may be exposed to risk, classified in different ways by international law, but are all affected by uses of Al technologies.

Working with UN agencies and other partners, this studentship will research responsible Al technologies in forced displacement contexts in relation to prediction, protection, and the reduction of vulnerability for people on the move. The student will either work on AI Methods to detect and predict forced displacement or on privacy protected edge computing and machine learning to protect vulnerable people.

Prediction of Movement

Here the student will develop AI models for accurately predicting forced displacement that foreground the reduction of vulnerability, layering in modes of responsibility to inform human decision making on the rapid provision and targeting of humanitarian support based on real-time data gathering.

Current models lack a real-time element and often use delayed or second / third hand sources that are difficult to verify, and even where relying on satellite imagery, primarily focus on estimating displaced population sizes and movements at settled destinations, leaving major gaps in understanding and predicting the dynamics of forced displacement.

This work will focus on providing real-time capability by adding real-time data of people movement from into AI models, augmented by sources such as AI based detection of human rights violations from social media, novel Satellite Remote AI Sensing which leverage satellites AI vision for a macroscopic view, providing valuable context by detecting structural causes that trigger movement like environmental changes such as drought, but also signs of emerging conflicts and aiding in the early stage detection of large-scale movements triggers.

Privacy-Enhancing AI Edge Computing

To enable privacy and biometric protection, the studentship will research novel edge computing architectures based on state-of-the-art research, protected by homomorphic encryption systems, to enable local Machine Learning fusion on the platforms minimising the transmission of sensitive information for enhanced security and reduced vulnerability.

This work will also extend deep learning fusion models into decentralised paradigm using federated learning on the edge platforms to train models on decentralised data sources, without compromising individual data privacy or using raw data.

The successful applicant will receive a full PhD Studentship, including full tuition fee discount for three years of study, a stipend payment towards your living costs and Proficio funding towards training, conferences, and travel.

Funding

The funding for this studentship consists of:

• Home or Overseas tuition fee waiver.
• Living costs stipend at the UK Research and Innovation recommended level per year. The stipend amount for 2025-26 is £20,780.

In addition you will receive a training bursary via funding, which may be used to cover the cost of advanced skills training including conference attendance and travel.

How to apply

You can apply for this postgraduate research opportunity .

Applicants should select full-time ‘PhD in Computer Science Oct 25’ when applying and (when asked for your ‘proposed research topic or area of research’) state the studentship title at the top of this advert.

Applications MUST include the following documents to be uploaded:

• Research proposal of no more than 2 pages (Font 11, Single line spacing), outlining suggested research focus(es), approach(es) and/or studies that applicants would be interested to include within this PhD.
• CV including research experience, skills, and publications (if any).
• Link to a repository (e.g. Github) that evidences your coding project experience.
• Covering letter outlining how you meet the criteria for the studentship.
• Transcripts of any undergraduate or masters’ programmes.

Application closing date: 23 May 2025.

Full Details

Please read the full terms and conditions before applying for or accepting a scholarship or bursary. They contain important information about eligibility, how to apply and how you'll receive your funding.

• CSEE Responsible AI Studentship T&Cs 2025-26.pdf

If you're a current student looking for information about an award you received, see our terms and conditions for previous years.