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technologies and processes. 2. Quantification of environmental impacts. 3. Statistical analysis. 4. Systems optimization. 5. Dissemination of results. Requirements Research FieldEngineering » Chemical
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exposure methods to enhance comprehension of material corrosion in hypersaline environments. Reliable test methodologies and statistical analysis techniques will be employed to assure conclusive
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trajectories. Leveraging deep learning, generative models and cross-modal autoencoders and working with both structured and unstructured data, the goal of this project is to develop a statistical framework and a
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in the development of cutting-edge statistical methods and machine learning algorithms inspired by massive healthcare datasets. Key Responsibilities Develop innovative statistical methods and machine
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analysis in biomedical data, in affiliation to the Artificial Intelligence Research Centre . The successful candidate will develop statistical and machine learning techniques to analyse biomedical data. High
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usefulness of the forecast, and perception of forecast performance by the public. Statistical post-processing techniques can help to reduce forecast errors by training machine learning models on data sets
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, outcomes, and real-world impact. The student will be supported by a supervisory team including Professors in hepatology and epidemiology, and experts in computational statistics and model fitting using
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occurrence, aetiology, outcomes, and real-world impact. The student will be supported by a supervisory team including Professors in hepatology and epidemiology, and experts in computational statistics and
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We are looking for a statistical geneticist to join the Cardiovascular Epidemiology Unit (CEU), Department of Public Health and Primary Care at University of Cambridge. The postholder will be
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experience in healthcare-related disciplines. They should be proficient in data analysis using statistical or computer modelling software, such as Stata or R. They should have an excellent command of written