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We have 78 neural networks PhD Projects, Programmes & Scholarships
neural networks PhD Projects, Programmes & Scholarships
We have 78 neural networks PhD Projects, Programmes & Scholarships
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Next-Generation Spiking Neural Networks Architectures and Machine Learning Algorithms
Spiking Neural Networks (SNNs) represent the so called ‘third generation’ of artificial neural network models, that bridge the gap between neuroscience and artificial intelligence by relying on biologically realistic models of neurons and network architectures to carry out computations. Read more
Autonomous Drone Surveys and Convolutional Neural Networks for Bridge Maintenance: A Predictive Approach Using Finite Element Analysis
Objective. The primary objective of this research is to develop a comprehensive framework for bridge maintenance that integrates autonomous drone surveys, convolutional neural networks (CNNs), and Finite Element analysis. Read more
Computational neuroscience: Statistical signal processing for multivariate neuronal data, Neural computing with Spiking neural networks.
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. Read more
Embedding physical models into deep neural networks for sonar detection (co-funded by SeeByte Ltd)
Deep Neural Networks (DNNs) achieve the state-of-the-art performance in several tasks in computer vision, natural language processing, image reconstruction, and many other areas. Read more
Building connections between model predictive control and neural networks
Feedback control enables dynamical systems to interact autonomously and safely with the real-world. The classical approach to feedback control system design involves first identifying a model of the system and then solving an optimal control problem to generate a control policy. Read more
PhD in Machine learning: Neural Networks to build a superior biofluids solver
PhD in Machine Learning. Unlocking the Future of Medical Diagnostics. Join our Dynamic Team in Sydney, Australia. Introduction. Read more
Neural Networks for Complex Dynamical Systems
Details. Dynamical systems are often solved/integrated by a suitable numerical discretisation method in such a way that certain properties of the underlying systems will be preserved. Read more
PhD Scholarship in spiking architectures for event-based computing
The recent advances in spiking neural networks (SNNs), standing as the next generation of artificial neural networks have demonstrated clear computational benefits over traditional frame/image-based neural networks. Read more
A novel energy-efficient spiking neural network for real-time intrusion detection
Summary. This groundbreaking research project addresses the pressing need for enhanced network security in our increasingly AI-driven world. Read more
Last chance to apply
Large-scale photonic-electronic integration for next generation neuromorphic computing systems
Start date. October 2023. Duration. 3.5yrs. Description. Neuromorphic computing has gained huge momentum in the last decade thanks to the emergence of novel machine learning algorithms such as deep learning. Read more
Neuromorphic RF sensing and processing
Artificial Neural Networks (ANN) have been a research topic since 1940's, even though it is only after the formalisation of the backpropagation algorithm for ANNs in 1986, that it has become possible to effectively train a non-linear ANN with multiple hidden layers of neurons. Read more
Effects of insecticides on brain activity
Project Overview. Overview . This project is an exciting new collaboration between the University of Reading and Syngenta Group, to investigate the mammalian toxicity of insecticides and understand their effects on the brain. Read more
Deep Learning and Explainable Artificial Intelligence for Flood Prediction (FAC23/EE/CIS/WOO)
Flood prediction models are crucial for managing extreme events and assessing hazards. However, current models often make simplified assumptions and rely on specific datasets. Read more
Develop a machine learning approach to multimodal imaging
The emergence of antimicrobial resistance (AMR) threatens to undo the advances of modern medicine, making commonplace interventions like major surgery or cancer chemotherapy effectively impossible. Read more
Learning a universal biomolecular force field
Molecular dynamics has been successful in obtaining biological insights by simulating the motion of biomolecules. Increases in compute power mean that simulations will soon reach the time scales on which much of biology happens. Read more
