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on clinical complications, and use machine learning to develop and validate predictive models to identify high-risk patients. The research aims to individualise inpatient care, reduce hospital-acquired
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backgrounds such as AI, computer vision, computer graphics, machine learning, robotics, wearable technologies, textile engineering, fashion technology, digital fashion, or related areas are encouraged to apply
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Are you fascinated by how machine learning can enhance control without compromising safety or stability? As a PhD candidate, you will develop scalable methods for expressive and flexible neural
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machine learning. You will develop and evaluate AI-driven visual speech recognition models and contribute to their integration into a smart-glasses prototype. The system aims to convert non-vocalized lip
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or AI/machine learning/NeuroAI. For candidates with a neuroimaging background, this may include experience with data acquisition, preprocessing, and/or analysis; experience with fMRI is preferred, but
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multidisciplinary environments Curiosity-driven and self-motivated working attitude Knowledge of biomechanical modeling, anatomy, vision-based motion capture, machine learning, control systems Keep in mind
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at Saxion University of Applied Sciences, you will contribute to the development of machine-learning models that connect powder characteristics and process parameters with the properties of the final
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Fully Funded PhD Studentship (UK Students Only) Real-Time Sub-THz Electromagnetic Sensing and Machine Learning for Dynamic Particulate Characterization University of Birmingham with support from
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. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale
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modern machine learning, statistical signal processing, or optimisation to turn heterogeneous knowledge (channel/network state, maps and topology, mobility, hardware constraints, and task-level KPIs