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at the Barts Cancer Institute (Queen Mary University of London). This role will involve analysing existing spatial-omics data sets and developing novel computational tools to understand the risk of developing
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of bioinformatics tools for RNA sequencing data analysis. Previous and experience with RNA sequencing, including sample preparation. Downloading a copy of our Job Description Full details of the role and the skills
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and evaluation. The post holder will take a leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute to the design and analysis
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range of methodologies, including econometric analysis, micro-econometric demand analysis, big data analysis, health macroeconomics, discrete choice experiments, cost-effectiveness analysis, decision
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assess the perceived and observed effects of government policies on cloud adoption, infrastructure investments, and data centre location decisions. Contributing to quantitative analysis of policy impacts
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spectrometry and ion activation strategies (e.g. EID and photo-dissociation). The project will entail improving the current capability of glycoprotein structural analysis using bottom-up mass spectrometry
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-seq data. For the duration of the project, you will be supported by a highly experienced PDRA specialising in mass spectrometry and data analysis, who will assist in overseeing daily operations
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optoelectronics or closely related field. Electro-optical characterisation and measurement of detectors are essential. General tasks will involve scientific research; analysis and interpretation of data; daily
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complements LCAB’s research programmes. High competence in the analysis of large ecological and other data sets and time series. Ability to write up research for publication in high profile journals, along with
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specialist knowledge and experience in data acquisition, signal processing, and data analysis from wearable or non-wearable sensors and devices. You must be able and willing to travel away from Oxford, often