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Field
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investigating the neural and computational basis of anergia and effort hypersensitivity in depression. You will be responsible for: conducting behavioural, ambulatory smartphone-based and neuroimaging assessments
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cancer using graph neural networks. Our current efforts extend this to additional cancers and modalities, such as multiplexed immunohistochemistry (mIHC), immunoflouresence, spatial transcriptomics and
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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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) 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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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