87 neural-network positions in United Kingdom

PhD Studentship - A Novel Energy-Efficient Spiking Neural Network for Real-Time Intrusion Detection

. Traditional methods struggle with real-time network traffic, especially on lightweight, low-power, resource-constrained edge devices. This project aims to explore the potential of spiking neural networks (SNNs

Post-doctoral researcher on Skyrmionic Artificial Neural Networks

Neural Network hardware. -> You will be part of a dynamic team in Manchester with access to specialised laboratory space for thin film deposition and magnetic/electrical/high frequency characterisation and

PhD Studentship in the Topic of Machine Learning (Learning Theory or Trustworthy Machine Learning)

include but not limited to Statistical-computational gap in modern machine learning Robustness of neural networks for trustworthy ML systems Fine-tuning of modern machine learning models The successful

Postdoctoral Research Assistant in Machine Learning, Bioinformatics and Genome Editing

neural network based methods for designing genome editing strategies to treat rare diseases. The postholder will join a large multidisciplinary team covering molecular methodologies, genomics, single cell

Research Associate

, to develop a novel end-to-end neuromorphic design approach and efficient hardware architecture based on spiking neural networks (SNNs). The project aims to develop novel computing solutions for the defence and

PhD Studentship: Reinforcement Learning and Generative AI for Future Wireless Networks

techniques to enable intelligent control in Open Radio Access Network (O-RAN) systems. Based at the University of Bristol and fully funded by EPSRC and BT for 4 years, this PhD project sits at the intersection

Research Associate in Geometric ML

of Oxford. You will be expected to conduct novel research in geometric ML, in particular on graph neural networks and geometric generative models and applications to molecular design. You will also provide

PhD Studentship: Machine Learning Models for Subgrid Scales in Turbulent Reacting Flows

Project title: Machine Learning models for subgrid scales in turbulent reacting flows Supervisory Team: Temistocle Grenga, Ed Richardson Project description: Supervised deep convolutional neural

PhD Studentship: Quantitative Logics and Complexity Theory

/entities/document/10.4230/LIPIcs.MFCS.2023.60 https://dl.acm.org/doi/10.1145/3531130.3533360 Another direction is to develop logics for understanding the limitations of neural networks as an abstract

Research Associate in Geometric ML

Computer Science Department, located in the heart of Oxford. You will be expected to conduct novel research in geometric ML, in particular on graph neural networks and geometric generative models and applications