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analysis will focus on building sophisticated Deep Learning models, e.g., Long Short-Term Memory (LSTM) networks, to accurately model DPs over time and predict mood deterioration. The project will implement
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. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in 2D convolutional neural networks in Python. This is a part-time position (5
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inaccurate, and rain gauge networks, while reliable, are too sparse to capture highly localised storms. Reliable, high-resolution rainfall data is urgently needed to improve flood prediction, climate
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(SFDI) and also from our custom-built photoplethysmography (PPG) sensor. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in
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networks continuously generate sensitive data. You will investigate adaptive learning frameworks – such as multi-agent or multi-task approaches – that ensure robustness, explainability, and trust across
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Description DC6: Molecular and tissue determinants of adverse tissue outcomes in hearing implants. Join the prestigious Marie Skłodowska-Curie Actions (MSCA) Doctoral Network SHIELD (Strategies for Healing
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This is a four-year (1+3 MRes/PhD) studentship funded through the Cambridge EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment: Unlocking Net Zero (FIBE3 CDT). Further
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for research excellence and interdisciplinary systemic thinking for Net Zero. The ReNU+ vision is that they will become living examples of a highly skilled workforce delivering an equitable energy transition so
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engineering, computational neuroscience, artificial neural networks and bio-inspired robotics: "Rhythmic-reactive regulation for robotic locomotion" (Supervisor: Prof Fulvio Forni) will apply techniques from
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Northern Advanced Research Training Initiative network. How To Apply Please see https://www.ncl.ac.uk/postgraduate/fees-funding/search-funding/?code=nubs012 for application instructions. Shortlisted