Postgraduate Research


PhD student working at computer

Find the right opportunity for you   

Are you looking for a postgraduate research opportunity?

We're offering a wide variety of studentships and other research opportunities throughout the year. As a research student, you'll work with at least one academic supervisor and be given research training to help you gain skills in research methodology.

Take a look at our current opportunities below.

Current Faculty of Science and Health studentship opportunities

Text analytics with deep learning for predicting dynamic economic systems

The goal of the PhD studentship is to develop novel text analytics methods based on modern Deep Neural Networks. The novel text analytics methods will be applied to better understand and analyse the complexity of real-world activities and dynamics in socio-economic systems such as stock markets, health care services, innovation processes and others.

Lead department: School of Computer Science and Electronic Engineering (in collaboration with the Department of Mathematical Sciences)

Closing date: 30 August 2019

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Demand scheduling and emission control in e-fulfilment

This project aims to apply machine learning techniques to understand major cost/emission factors of vehicle routing subject to customer choices, so as to generate effective incentive schemes to schedule customer demands. It also aims to provide resolutions on the efficient usage of electric fleets in grocery delivery. 

Lead department: Department of Mathematical Sciences (in collaboration with the School of Computer Science and Electronic Engineering)

Closing date: 12 September 2019

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Departmental studentships - Computer science and electronic engineering

Modelling beliefs dynamics of social media users with machine learning methodologies

This project aims to develop a Virtual Social Media Companion that educates and supports teenage school students facing the threats of social media such as discrimination and biases as well as hate speech, bullying, fake news and other toxic content. The companion will raise awareness of potential threats in social media among students without being intrusive. It will apply gamification strategies and educative information selection algorithms.

Department: School of Computer Science and Electronic Engineering

Closing date: 30 August 2019.

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