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Field
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duties will include contributing to the provision of statistics, research methodology, and laboratory classes to year one and year two undergraduates, in addition to a number of designated administrative
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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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of the underlying the physical processes leading to predictability and forecast skill of North Atlantic cyclones. The project will also explore statistical post-processing methods to see if raw forecast skill can be
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electrolyser devices. However, we do not expect you to have prior knowledge of fabricating and running electrolysers or computational models. You will get the opportunity to learn from both the experienced
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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. The ideal candidate will possess strong statistical and analytical skills (as mentioned above) and excellent English communication abilities. He/she should also demonstrate the ability to assess datasets, use
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computational and machine learning approaches to integrate Oxford Nanopore (ONT) long-read data with bulk and single-cell RNA-seq profiles. The aim is to identify host-microbiome molecular signatures that drive
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marginal structural models will be extended with machine learning techniques for counterfactual prediction and to support sensitivity analyses Candidate The studentship is suited to a candidate with a strong
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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
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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