School of Computer Science and Electronic Engineering

Scholarships and funding

Supporting your study

We offer many funding opportunities to support both undergraduate and postgraduate students, including a broad range of University of Essex scholarships and studentships.

Scholarships

To find out more about our scholarships, use our scholarship finder to see what is available. 

PhD Studentships

Studentships are available through the School, through the Faculty of Science and Health (both listed below), and also through IGGI, our Doctoral Training Centre. We are delighted to announce the following PhD studentships for 2018: 

Semantic Brain-computer interfacing

This PhD will investigate novel machine learning techniques to develop a new type of BCI that has the potential to be both highly intuitive and allow greater levels of accuracy and communication speed than possible with current BCI. This will result in a semantic BCI, which will be built based on simultaneous recording of EEG and fNIRS.

The successful applicant will be supervised by Dr Ian Daly and Professor Riccardo Poli and will be part of the Essex Brain-Computer Interfacing and Neural Engineering Lab: today the UK’s largest research group in brain-computer interfaces.

Deadline: Friday 23 February 2018

Bayesian Deep Learning for Alzheimer's conversion prediction in Mild Cognitive Impairment subjects

Mild cognitive impairment (MCI) is a transitional state between normal ageing and dementia. In a number of cases, MCI carries the risk of conversion to Alzheimer’s disease-related dementia. MCI typically includes slowing of motor performance and information processing, impaired attention and impaired executive functions with partial preservation of memory. Machine learning techniques have recently been identified as promising tools in neuroimaging data analysis and can, to a certain extent, work on a single patient basis in predicting conversion from MCI to Alzheimer’s disease (AD). This PhD will investigate novel convolutional and Bayesian deep learning techniques to identify biomarkers of MCI and improve the accuracy of detecting early signs of the potential for MCI to progress into AD. Early AD diagnosis is important for giving access to treatments that can improve symptoms and slow down the progress of the disease.

The successful applicant will be supervised by Dr Luca Citi and Dr Alba Garcia and will be part of the Essex BCI and Neural Engineering Lab (http://essexbcis.uk): today the UK’s largest research group in brain-computer interfaces.

Deadline: Friday 23 February 2018

Communications and Networks (2 studentships available)

PhD applications that address any challenges of 5G and beyond systems or future networks will be encouraged. Specific list of topics are, but not limited, to include:

  • IoT communications
  • Network/Cyber- security
  • Wireless power transmission/Energy harvesting
  • VR/AR communication
  • Network coding
  • Machine assisted rate allocation
  • Fog/Cloud computing
  • ICN/NDN/SDN
  • Microwave and mm-wave circuits, systems and antennas for future mobile and satellite communications
  • Numerical modelling in electromagnetic communications and radars
  • Novel tunable material characterization with applications in multi-frequency communication systems
  • Optical transmission/Visible light communications

Deadline: Friday 23 February 2018

Intelligent Operation of Internet of Things Devices using Multiple Unmanned Aerial Vehicles

This PhD focuses on efficient and intelligent operation of IoT devices using UAVs by developing novel schemes for autonomous adaptive management of connectivity and coverage while maintaining the energy of the UAVs and IoT devices. Computational Intelligence techniques such as fuzzy systems, spatial-temporal reasoning and collective and cooperative intelligence approaches (multi agent and collective optimization techniques) will be investigated.

The successful applicant will be supervised by Dr Mohammad Hossein Anisi, Dr Faiyaz Doctor and Professor Hani Hagras.

Deadline: Friday 23 February 2018

Algorithmic and Computational Aspects of Economics and Finance

The expansion of the Internet has brought about an exciting research area full of unexplored, well-motivated research directions. Its use as a primary computing platform has changed the way computation is now performed. In many contexts, reaching an outcome or a collective decision depends on information that is privately controlled by several agents, who have an interest in the actual outcome that will be realized. As a result, any individual agent might benefit, to some extent, by falsely reporting his private information. This, in turn, can greatly affect the performance of the overall system.

The goal of this project is to explore settings of strategic interaction in the intersection of computer science and economics: To what extent does the behaviour of the individual agents affect the overall outcome? Can the agents be motivated towards a more desirable behaviour? Algorithmic Game Theory is a perfect tool to model and analyse such competitive situations. It can be applied to examine the incentives of users in several different contexts like scheduling, block-chain applications, or finance, among others.

The successful applicant will be supervised by Dr Maria Kyropoulou and Dr Carmine Ventre.

Deadline: Friday 23 February 2018

What do you mean? Automatic identification of language barriers for effective communication with government organisations

The goal of this project is to use data science and natural language processing to investigate techniques for building language profiles of specific groups of speakers. Clustering methods will be employed to define target clusters of speakers on the basis of information about their age, socio-economic context, and education, and based on their language profiles methods for automatic readability assessment will determine whether information from a given document is optimally written for targeting that group. This will enable the use of information customisable to the language abilities of different groups of speakers, and can be used to increase accessibility to digital content.

The successful applicant will be supervised by: Dr Aline Villavicencio, Dr Renato Amorim and Professor Slava Mikhaylov.

Deadline: Friday 23 February 2018

Energy Efficient and Reliable Computer Vision Processing on Multi-core Processors

Computer vision applications are key candidates to exploit these multi-core processors as they provide parallel processing capability, which can help to achieve the desired levels of performance, e.g., frames per second (fps) requirement needs to be satisfied for a video processing, especially when implementing deep learning architecture to provide state of the art computer vision capabilities. These applications are potential candidates for several systems such as industrial robots and autonomous vehicle. However, management of application(s) on multi-core processors impose several challenges when trying to optimize for both energy consumption and temperature while satisfying the requirements at the same time. This PhD topic plans to address these challenges and some of them are as follows:

  • Finding appropriate number and types of cores to be used for different computer vision tasks such as acquiring, processing, analysing and understanding digital images using deep learning and convolutional neural networks.
  • In case of multiple applications, finding appropriate number and types of cores to be used for each of them.
  • Identification of appropriate operating voltage/frequency of the cores as the multi-core processors also support dynamic voltage and frequency scaling (DVFS)
  • Appropriate partitioning of application (or task) threads between CPU and/or GPU cores in case the application (or task) needs to jointly exploit the CPU and GPU.

Devising solutions in terms of algorithms to address these and other identified challenges will be the main focus of the PhD. The solutions will need to be evaluated by considering a set of applications, mainly computer vision domain, and a suitable multi-core platform such as Samsung Exynos 5422 SoC and other platforms available in the EIS Laboratory.

The successful applicant will be supervised by Professor Klaus McDonald-Maier, and Dr Amit Singh.

Deadline: Friday 23 February 2018

CAREOBOT- Cognitive Robotic Companion for Carehomes and Hospitals

CAREOBOT aims to develop a first prototype of cognitive architecture for Pepper humanoid enabling it to “act, interact, learn and assist” both caregivers and patients in carehomes. The research will advance the state of the art in several core areas of cognitive robotics:

  1. Mobility and dextrous manipulation in unstructured natural living spaces (like hospitals, homes);
  2. Cumulative learning and Reasoning to cater to diverse tasks, users;
  3. Social intelligence while interacting with humans.

The project will both exploit and augment built in features of Pepper like NAO Qi Motion, People Perception, Gaze and Face tracking, Speech recognition/generation, interactive tablet to create a lively autonomous companion for care homes. Experiments and testing will be conducted in both the robotics arena or iSpace initially and later directly at the end user sites i.e. Hospitals collaborating with us on this theme.

The successful applicant will be supervised by Dr Vishuu Mohan and Dr Dimitri Ognibene.

Deadline: Friday 23 February 2018

Studentships within the Faculty of Science and Health

Rapid 3D Reconstruction of Coral Ecosystems from Multi-Camera Imagery

By using camera systems taking images from multiple angles, compelling 3D models of coral reef structures can be built using a photogrammetry technique called “structure from motion”. An alternative is a technique known as “visual SLAM”, which runs in real time. By adopting elements of both techniques, this studentship will develop a hybrid approach to support marine surveying, in particular for threatened coral reefs. For example, by producing 3D models of reefs, biodiversity can be monitored in ways that are currently not possible using manual measurements taken in-situ by divers.

The successful applicant will be supervised by Dr Jon Chamberlain and Dr Adrian Clark (in CSEE), with regular input from Professor David Smith and Dr Philippe Laissue (in the School of Biological Sciences) and will be part of a growing interdisciplinary group focusing on state-of-the-art computer applications in marine sciences.

Deadline: Friday 23 February 2018

Hybrid Deep and Fuzzy Logic Based learning for functional genomics

The aim of the project is to design and develop hybrid models of Deep Learning and white box models based on fuzzy logic systems to predict, explain and analyse the impact of genomic mutations on gene expression using new data generated by STARR-seq experiments. Using these methods, we expect to unveil causal mechanisms of complex diseases, such as cancer.

The successful applicant will be supervised by Dr Giovanni Stracquadanio, Dr Madapura and Professor Hani Hagras. 

Deadline: Friday 23 February 2018

Computational Intelligence for Developmental Cognitive Neuroscience

The PhD fellowship will involve the development of computational models and algorithms for the analysis of neuroimaging data, enabling a framework that objectively deals with uncertainty, partial truth and ambivalence of brain function without jeopardizing expressiveness and interpretability. This novel computational framework will provide functions to extend the current methodology for the analysis of brain activation, functional and effective connectivity in neuroimaging methods that measure hemodynamic responses associated with neuron behaviour.

The applicant will also have the opportunity to perform experimental research in the area of developmental cognitive neuroscience that aims at studying the underlying and unknown cognitive mechanisms behind self-awareness, perception and consciousness in the early stages of human brain development. The University’s centre for Brain Science accounts for one of the most comprehensive and equipped laboratories for Developmental Cognitive Neuroscience in the UK’s, which has been featured in the media.

The successful applicant will part of The Centre for Computational Intelligence and supervised by Dr Andreu-Perez and Prof Hagras and also part of The Centre for Brain Science and supervised by Dr Rigato and Dr Filippetti.

Deadline: Friday 23 February 2018

How to apply - PhD studentships

 You should apply via the Essex postgraduate applications portal, ideally making contact with potential PhD supervisors before applying.

You can also find out more about our current areas of research on our research interests page and on our staff profiles.  

Questions and queries about the studentships should be sent to the CSEE School Office (csee-schooloffice@essex.ac.uk).

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