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) AI algorithms and deep neural networks (including deep learning frameworks such as TensorFlow or PyTorch etc.). f) Basic neuroscience (including knowledge of neuronal functioning and neural circuits
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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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theoretical research is focused on embodied neuroAI, recognising that the body influences biological neural networks, the continuity of actions, and sensory inputs. Leveraging advancements in Drosophila genetic
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learning solutions as well as the challenges of using neural networks as representations of quantum states. You will be given an increasing amount of scientific freedom supported by structured mentoring
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the visual system. The goal is to create a robust mechanistic neural network model of the visual system that not only mimics its processing capabilities but also its adaptability, leveraging early
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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., neural networks, Gaussian processes, active learning) interest in materials science (e.g., SCC) excellent knowledge of English (written and spoken) high degree of motivation, creativity, and flexibility
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architectures and principles from Bayesian neural networks and biological sequence models, including large DNA and protein language models. The project also aims to develop a prototype federated learning
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to machine learning and deep neural networks, into the DG finite element solver to reduce computational costs while maintaining the accuracy. The key objective of this work will be to provide step-change
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-informed data analytics tools for the predictive maintenance (PdM) strategy applications to high-value critical assets. Among others, the recently developed Physics-informed Neural Network (PINN) technique