University of Leeds Featured PhD Programmes
University of Edinburgh Featured PhD Programmes
King’s College London Featured PhD Programmes

neural engineering PhD Projects, Programs & Scholarships

We have 78 neural engineering PhD Projects, Programs & Scholarships

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  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.
  Neural dynamics of drug-paired memory in the mouse brain
  Dr D Dupret
Application Deadline: 22 April 2020

Funding Type

PhD Type

Applications are invited from UK or EU residents to join a multidisciplinary team of scientists studying brain network mechanisms supporting memory-guided behaviour.
  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.
  Compressed learning techniques for next generation connected autonomous mobility
  Research Group: Communication Technologies
  Dr Y Ko
Applications accepted all year round

Funding Type

PhD Type

The exploding data traffic is expected to grow at around daily 3 Petabytes by 2022, by a range of the emerging autonomous mobility applications such as connected autonomous vehicles, healthcare autonomy, warehouse robots, and AR/VR entertainment.
  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.
  AI Hardware for Symbol-level Processing
  Prof T Prodromakis
Application Deadline: 31 August 2020

Funding Type

PhD Type

Supervisor. Themis Prodromakis. Co-supervisor Alex Serb. Project description. The Centre for Electronics Frontiers at the University of Southampton is a dynamic interdisciplinary Centre that delivers novel solutions for advanced sensory systems, energy technologies and unconventional computing architectures.
  Neuromorphic Sensing Processing
  Prof T Prodromakis
Application Deadline: 31 August 2020

Funding Type

PhD Type

Supervisor. Themis Prodromakis. Co-supervisor Alex Serb. Project description. The Centre for Electronics Frontiers at the University of Southampton is a dynamic interdisciplinary Centre that delivers novel solutions for advanced sensory systems, energy technologies and unconventional computing architectures.
  Investigating neuronal patterning and development
  Dr F Mackenzie, Prof A Walker, Dr N Hill
Applications accepted all year round

Funding Type

PhD Type

This 3-year PhD will study molecular and cellular mechanisms of neuronal patterning and neural development using ex vivo/in vitro methods (mouse/human tissue/cells) and/or in vivo methods (invertebrates).
  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.
  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.
  Cost-effective learning and signal processing for evolvable massive wireless communications
  Research Group: Communication Technologies
  Dr Y Ko, Prof AM Tyrrell
Application Deadline: 30 April 2020

Funding Type

PhD Type

Due to the rapidly increasing demand for a huge data, we are in the transition region from 4G (LTE-Advance) systems to 5G and beyond 5G systems.
  Self-Supervised Learning and Variational Inference for adaptation in Deep Learning
  Dr G Leontidis
Application Deadline: 1 June 2020

Funding Type

PhD Type

We are looking for a highly motivated prospective PhD student to undergo a 3-year fully funded PhD project on Machine/Deep Learning foundations/theory and applications within the Department of Computing Science at the University of Aberdeen (UoA).
  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.
  Perceptual stability in Virtual Reality across the adult lifespan
  Dr P Warren, Dr R Champion
Applications accepted all year round

Funding Type

PhD Type

When we move, stationary parts of the world around us move on the retina. Of course, usually we correctly interpret this retinal motion as due to our own movement and not movement of the surrounding environment.
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