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Field
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support to develop their research career beyond completion of doctoral studies. Applicants should have a primary degree in a relevant methodological discipline (such as statistics, health economics) or a
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/sociology/biology/environmental sciences/microbiology/statistics/veterinary or human medicine). Applicants whose first language is not English require an IELTS score of 6.5 overall with a minimum of 5.5 in
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calculations of well-characterized 2D materials, simulations of electron microscopy images, and machine learning methods to reconstruct the 3D atomic positions of materials from a 2D microscopy image. The
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techniques that are useful for the modelling of many real-life systems. These include the development and analysis of stochastic models, computer simulations, differential equations, statistical inference
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and accuracy, ultimately saving lives. This collaborative PhD project aims to develop and evaluate advanced deep learning models for speech and audio analysis to predict Category 1 emergencies
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replacement, statistics and working with joint registry data. You will be based in the Integrative Musculoskeletal Biomechanics (IMSB) research group and join a thriving PhD research community in the School
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FTE, 30 hours per week minimum) and is fixed term up to 31/03/2027. About You You will have extensive expertise in statistics, likely including a PhD, and experience and interest in working in
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, applied statistics, biomedical sciences, health services research, or a medical degree with relevant experience, or equivalent professional experience. Demonstrated proficiency in quantitative methods and
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the genetic factors influencing changes in brain structures, using brain imaging, computational and statistical methods of network science. Project Aim: The aim of the project is to uncover the complex
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine