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technology, power, and politics. Particular merits include: Skills in relevant methods, such as qualitative text or discourse analysis, digital ethnography, quantitative analysis of digital data, social
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methods for single-cell data analysis (tools developed by the team : https://github.com/cantinilab ). Single-cell high-throughput sequencing, extracting huge amounts molecular data from a cell, is creating
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such as landslide movement style, runout, and how landslide hazards evolve over time. This Ph.D. project will leverage the analysis of new time-series data from cloud-based satellite image archives
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, climate physics, geosciences or a related field; excellent skills in scientific programming and numerical / statistical analysis of simulated and observed data; a versatile mind and openness to work on a
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: The doctoral candidate will perform computational analysis of a combination of multi-omics data from the gut microbiomes of patients with Alzheimer's disease or Parkinson's disease. The candidate will use state
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-field methods) Multiscale mechanics and microstructure-property relationships Python/C++/Matlab-based simulation and data analysis Industry-facing research and technology transfer You will also benefit
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such as scRNA-seq, bulk RNA-seq, DNAm analysis, proteomics and clinical data integration to determine how IFN-I responses change during ageing. Application Procedure Applicants should submit a cover letter
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Experience analyzing and interpreting large-omic datasets. Multi-disciplinary experience in integrating cellular and molecular mechanisms and data with phenotypic, physiological, and psychological data
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software) and data analysis, and a keen interest in the intersection between environmental sustainability, computing, and science. They should have an ability to communicate and present results to other
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analyses by age, ethnicity, and index of multiple deprivation will be performed. The second stage of the study will involve the analysis of prospectively collected EQ-5D-5L data from a cohort of patients to