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Computer Science & IT (neural engineering) PhD Projects, Programs & Scholarships

We have 44 Computer Science & IT (neural engineering) PhD Projects, Programs & Scholarships

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  Computational neuroscience: Statistical signal processing for multivariate neuronal data, Neural computing with Spiking neural networks.
  Research Group: Intelligent Systems and Nanoscience
  Dr D Halliday
Applications accepted all year round

Funding Type

PhD Type

How does the human brain work? As you read this text, the pixels on your screen are converted into a series of spike trains passing along the neural pathways in your brain, allowing you to understand the content.
  Information Extraction from Scientific Texts to support Nature-Inspired Engineering (EPS2020/39)
  Prof M Desmulliez
Application Deadline: 28 February 2020

Funding Type

PhD Type

Nature has inspired various highly innovative engineering solutions, such as Velcro, the Japanese bullet train and others. Solving engineering problems using Nature requires however specific and interdisciplinary domain knowledge.
  The mathematics behind the smartness of neural networks
  Dr M S Baptista
Applications accepted all year round

Funding Type

PhD Type

Intelligence is one of the pillars that allows animals to master their environment. Scientific approaches recently proposed have been capable of simulating networked systems that reproduce similar emergent manifestations of behavior as those observed in the brain.
  PhD: Dynamic wavefront engineering via epsilon-near-zero nonlinearities (Optalysys Ltd)
  Research Group: CDT in Applied Photonics
  Prof D Reid, Dr M Ferrera
Application Deadline: 28 February 2020

Funding Type

PhD Type

Project Description. The proposed project involves the ASN research lab led by Dr. Ferrera (Heriot-Watt University) and Optalysys a world leading company in ultra-fast optical information processing.
  Real-time simulation of large neural aggregates
  Research Group: Sustainable Electronic Technologies
  Prof A Brown
Application Deadline: 31 August 2020

Funding Type

PhD Type

Supervisor. Prof Andrew Brown. Co-supervisor Dr Graeme Bragg,. Project description.
  Global resilience assessment using artificial neural network
  Research Group: Future Cities Research Network
  Dr M Imani
Applications accepted all year round

Funding Type

PhD Type

Research Group. Future Cities Research Network - https://www.anglia.ac.uk/science-and-engineering/research/institutes-and-groups/future-cities.
  NEUROSCAPE - a Virtual Reality Artificial Environment for simulated neural development studies
  Research Group: Sustainable Electronic Technologies
  Prof A Brown
Application Deadline: 31 August 2020

Funding Type

PhD Type

Supervisor. Prof Andrew Brown. Co-supervisor Dr Graeme Bragg, Professor John Chad. Project description.
  Animats - neural culture controlled robots for closed-loop neuroscience
  Prof S Nasuto, Dr E Delivopoulos
Applications accepted all year round

Funding Type

PhD Type

The project will continue research started at the University of Reading on animats - robotic platforms controlled in real time by activity of biological neuron cultures and will investigate the methods for implementing efficient conditioning protocols.
  The dynamics of human attention in the online environment
  Dr M-A Rizoiu
Applications accepted all year round

Funding Type

PhD Type

Topic. This project will develop and evaluate stochastic models for the spread of information over complex social networks, in which the individual actions -- such as retweets, posts or shares -- are only partially observed.
  Optimisation methods for (deep) neural networks
  Dr C Grosan
Applications accepted all year round

Funding Type

PhD Type

A machine learning algorithm can be viewed as a mapping between inputs and outputs, with the scope of optimising and tuning the parameters that describe it so that the performance is improved.
  Investigation of adversarial learning with deep neural networks
  Dr K Huang, Dr R Zhang, Dr Y Goulermas
Applications accepted all year round

Funding Type

PhD Type

Adversarial examples are augmented data generated by imperceptible perturbation of existing samples. They have recently drawn much attention with the machine learning community.
  Adaptive self-learning vision systems
  Dr P Kinnell, Dr N Lohse, Dr A Soltoggio
Applications accepted all year round

Funding Type

PhD Type

Many computer vision systems make use of artificial neural networks to undertake complex image analysis tasks, such as locating objects in a clutter scene.
  A Neural Fuzzy Fusion Engine for Human-Machine Autonomous Systems
  Dr YK Wang
Applications accepted all year round

Funding Type

PhD Type

Abstract. This project aims to develop an intelligent engine to adaptively fuse multiple trust-based information from various agents in human machine autonomous systems (HMAS).
  BioVision: bio-inspired computer vision using deep architectures (Advert Reference: SF19/EE/CIS/BELATRECHE)
  Dr A Belatreche, Dr F Khelifi
Applications accepted all year round

Funding Type

PhD Type

The primate visual system outperforms the existing automated vision systems in almost all measures.
  Protein ghost recognition using deep learning
  Research Group: Sustainable Electronic Technologies
  Prof A Brown
Application Deadline: 31 August 2020

Funding Type

PhD Type

Supervisor. Prof Andrew Brown. Project description. POETS (Partially Ordered Event Triggered Systems) technology is based on the idea of an extremely large number (millions) of small processing cores, embedded in a fast, bespoke, hardware, parallel communications infrastructure – the core mesh.
  Machine learning for Autonomous and Interconnected Vehicles
  Research Group: Applied Computing
  Dr ARA Abdullatif, Dr S Kabir, Dr I Ghafir
Applications accepted all year round

Funding Type

PhD Type

Cyber-Physical Mechatronic Systems such as autonomous vehicles, and intelligent transportation rely heavily on machine learning techniques for ever-increasing levels of autonomy.
  Computer Vision and Machine Learning for ReIdentification of Civil Structural Components and their Damage Analysis.
  Dr B Mandal
Applications accepted all year round

Funding Type

PhD Type

In this project the PhD research student needs to understand the strengths and weaknesses of the deep convolutional neural network for analysis of civil infrastructures, such as concrete bridges, highways, buildings, tunnels and stadiums, using videos/images captured by a network of multiple cameras.
  Improving Transparency of Intelligent Systems to Support Safety Assurance for Autonomy, jointly funded by EPSRC and Jaguar Land Rover
  Dr N Griffiths, Dr T Popham, Dr A Bhalerao
Applications accepted all year round

Funding Type

PhD Type

PhD studentship (4 years fully funded). Improving Transparency of Intelligent Systems to Support Safety Assurance for Autonomy, jointly funded by EPSRC and Jaguar Land Rover.
  A combined framework of machine learning and full waveform inversion for electromagnetic sensing of infrastructure (Advert Reference: SF19/EE/MCE/WARREN)
  Dr C Warren, Prof J Martin
Applications accepted all year round

Funding Type

PhD Type

Electromagnetic sensing systems, such as Ground Penetrating Radar (GPR), are critical tools to allow us to evaluate the condition of surface and sub-surface infrastructure.
  Testing Robotics and Autonomous Systems Using Search-based Techniques
  Research Group: Applied Computing
  Dr RE Lefticaru, Prof D Neagu
Applications accepted all year round

Funding Type

PhD Type

Robotics and Autonomous Systems (RAS) are becoming indispensable for human productivity and it is estimated that there will be a £13 billion global market for RAS by 2025.
  Design of advanced coding and random access techniques for DNA storage
  Dr Z Liu, Dr N Thomos, Dr A Dumbrell
Application Deadline: 31 March 2020

Funding Type

PhD Type

Start date. October 2020. Duration. 3 years (full time). Location. Colchester Campus. Based in. School of Computer Science and Electronic Engineering (in collaboration with School of Life Sciences and Department of Mathematical Sciences).
  PhD in Computing Science: Data compression at the quantum limit for big-data machine learning applications
  Prof R Murray-Smith, Prof D Faccio
Applications accepted all year round

Funding Type

PhD Type

Background.
  Complex network control of stem cell derived cultures
  Prof S Nasuto, Dr E Delivopoulos, Dr Y Hayashi
Applications accepted all year round

Funding Type

PhD Type

"In order to create a next generation of interfaces between prosthetic devices and the nervous system or to create next generation of intelligent robots – hybrids between artificial and biological systems, it is necessary to understand the principles that allow biological networks to achieve control of bodies.
  Artificial Intelligence and Machine Learning for Biomedical Data
  Prof P Andras
Applications accepted all year round

Funding Type

PhD Type

Artificial Intelligence (AI) and Machine Learning (ML) are the leading edge approaches to data driven problems across all areas of life, technology and sciences.
  Machine learning-based nonlinear approximation of PDEs on complex domains
  Prof S Badia
Applications accepted all year round

Funding Type

PhD Type

Countless phenomena can be modelled by partial differential equations, including the electromechanics of the heart or option pricing in finance.
  Liquid Transistors: Real-time Probabilistic Computing with Non-linear Analogue Components
  Research Group: Intelligent Systems and Nanoscience
  Dr M Trefzer
Applications accepted all year round

Funding Type

PhD Type

"Perfection has always been expensive". An area where this is certainly true, and is rapidly becoming more so, is electronic systems design where the goal is to perform useful, stable computation.
  Research on Exoskeleton Control System based on Brain-Computer Interface
  Dr M Huang, Dr R Yang, Dr D Tate, Dr R Ferrero
Applications accepted all year round

Funding Type

PhD Type

To ensure the safe and reliable operation of exoskeleton, the control system based on brain-computer interface is the premise and foundation, which has been discussed in literature.
  Data analytics
  Prof AK Nandi
Applications accepted all year round

Funding Type

PhD Type

In recent years, more and more data are available in many areas. We live in a very data-rich world. Our challenge is obtain information or to develop understanding from such data.
  Deep Learning based Network Forensic Methods for Investigating Botnets in the IoT (Application Ref: SF19/EE/CIS/ASLAM)
  Dr N Aslam, Dr B Issac
Applications accepted all year round

Funding Type

PhD Type

The process of designing IoT protocols and sensors and the lack of standards are the main reasons why the IoT is an easy target for botnets.
  Deep Learning / Bioinformatics Approach for Protein-Protein Interaction Prediction
  Dr JC Nebel
Applications accepted all year round

Funding Type

PhD Type

Since most molecular processes rely on protein–protein interactions PPIs), knowledge of those interactions is extremely valuable for biomedical research and drug design.
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