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. This project will rely on recent advances in neural networks to develop machine learning potentials (MLPs) for MD simulations of realistic nanomaterial/coolant-liquids and use these to gain fundamental insights
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Appropriate computational skills and knowledge of programming languages (Python, C++, etc.) Experience with Machine and Deep Learning models and software (Keras, Scikit-Learn, Convolutional Neural Networks, etc
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Delays to Sculpt Network Attractors. This is a brain inspired project on networks of oscillators to model patterns of functional connectivity seen in large scale brain recordings. These describe
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network attractors, funded by The Leverhulme Trust. This is a brain inspired project in the field of Neurodynamics. Networks of oscillators are ideal candidates for modelling patterns of functional
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and prosthetic devices in the real-world. This PhD project offers the opportunity to work on pioneering research that combines state of the art computational modelling (deep neural networks) and
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approach could resolve this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat
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robots become increasingly integral in society, they must adapt to changing environments and cooperate with human partners effectively. Traditional AI systems, such as neural networks and deep learning
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. Work on this project will require research into novel methods that extract information from text that can be combined with the acoustic training data to inform flexible neural network structures and
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling