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health and social care challenges of the 21st century. We employ traditional statistical and epidemiological methods, alongside cutting-edge artificial intelligence algorithms to gain insights into real
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with a Ph.D. (either awarded or nearing completion) or equivalent professional qualification and experience in Machine Learning, Statistics, or a related field, who have in-depth knowledge in and
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Machine Learning, Statistics, or a related field, who have in-depth knowledge in and demonstrable experience with: Recent deep learning techniques, including, e.g., diffusion models and attentional models
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/Bayesian Modelling techniques to establish drivers of global sediment flux. They will use numerical models and statistical processing techniques to establish patterns and future trajectories of global change
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health and social care challenges of the 21st century. We employ traditional statistical and epidemiological methods, alongside cutting-edge artificial intelligence algorithms to gain insights into real
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skills in MATLAB or Python for noise and vibration analysis and be comfortable working with statistical methods to quantify characteristics of data sets. In addition to undertaking original, state
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will be comfortable working with statistical methods to quantify essential characteristics of complex data sets and ideally have practical experience in using machine learning approaches. In
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. The role requires expertise in MS-based metabolomics, MS method development, classical statistics, microbiomics, nutrition, and human biochemistry. This role will be involved in database management with