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novo neurogenesis followed by integration of new neurons into functional neural circuits. In humans, new neurons are made daily during learning, and these new neurons also integrate into functional
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filled. This fully funded PhD explores AI-native and sensing-aware wireless systems where communications and sensing are co-designed end-to-end. You will unify modern machine learning, statistical signal
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on the performance of the CMF; Using machine-learning (deep learning) methods to develop a predictive model and conduct the sensitivity study to investigate the multiple factors on the performance of flow meter
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to learn new techniques quickly and work independently. Very good communication skills and experience of working in a multidisciplinary team. Contact details Contact Name Professor Kim Watson Contact Job
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, neuroscience, machine learning, or related fields and/or merit/distinction-level performance in a relevant postgraduate degree (e.g. MSc) Experience of working in a neuroscience, clinical or engineering research
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an interdisciplinary approach to address this problem using dietary interventions on animal models of cognition. During this project you will learn techniques from a range of disciplines including: animal behavioural
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Software Development: Building the Next-Generation Trust Maturity Model Integrating DevOps practices into ML-driven systems: A Framework and Maturity Model for Continuous Machine Learning Development
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dynamic environments, utilising Vision, Language, and Action (VLA) models. The candidate will focus on designing novel training regimes and/or novel architectures for learning in embodied environments
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and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits
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properties of representative sediment classes. · Evaluate methods for predicting sediment type and physical properties from geophysical data using machine learning. · Assess the reliability