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, the adoption of AI in project management remains in the nascent stages. This PhD project will critically investigate how AI is shaping the management of projects and project studies. It aims to generate new
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Open PhD position: Autonomous Bioactivity Searching Subject area: Drug Discovery, Laboratory Automation, Machine Learning Overview: This 42-month funded PhD studentship will contribute to cutting
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
relevant field such as engineering, computer science, or applied mathematics. Experience or interest in AI, machine learning, or digital systems is beneficial. We welcome candidates from diverse backgrounds
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untapped potential remains in extracting value from this data. This PhD will explore advanced analytics techniques, including machine learning, digital twin modelling, time series analysis, spectral analysis
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PhD Studentship: Sleep and Circadian Rhythms in Elite Sport (Co-funded by Brighton & Hove Albion FC)
in machine learning methods for pattern recognition and prediction. An academic or practical background in sleep science and/or elite sport is highly desirable. The candidate will be embedded within
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, analytical and computer programming skills. Advantage will be given to applicants with experience in one or more of the following: signal processing, deep learning, acoustics, psychoacoustics, acoustic
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Open PhD position: Autonomous Bioactivity Searching Subject area: Drug Discovery, Laboratory Automation, Machine Learning Overview: This 42-month funded PhD studentship will contribute to cutting
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epidemiology and machine learning. The scholarship will fund course fees up to the value of home fees*, a tax-free stipend of no less than £20,780 per annum), plus additional support for research expenses
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Title: Predicting and Improving the Quality of Recycled Plastics Using Advanced Metrology and Data Science Research theme: "Materials Characterisation" "Data Science and Machine Learning in
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treatment processes through advanced machine learning, validated against physics-based models and experimental data. 2. System Integration: Integrating the DTs into material and energy balance equations