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, particularly in analysis of crosslinking and immunoprecipitation (CLIP) data. You will be a dynamic, creative, and motivated scientist interested in pursuing questions deeply. With a strong understanding of the
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the analysis of crosslinking and immunoprecipitation (CLIP), ribosome profiling and RNA proximity-ligation data (e.g. hiCLIP). We are looking for an innovative, and highly motivated scientist who is keen to
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development, data collection and multivariate analysis, regulatory compliance, and manuscript and abstract preparation. Fellows are also expected to engage in departmental and institutional seminars, continuing
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(PHHP) seminars, and other relevant educational programs. Data Analysis and Reporting: Prepare and distribute financial and non-financial reports to inform decision-making and project outcomes. Conduct
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analysis and solar-to-fuel/device performance evaluation. Fabricate and optimise catalyst electrodes and devices, with opportunities to use scalable roll-to-roll magnetron sputtering fabrication. Undertake
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. With Professor Cusack and the team, the postholder will participate in the full process from setting up awake infant fMRI, experimental design, data acquisition, analysis, computational modelling
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sustainable infrastructure. In this role, the successful candidate will develop and validate advanced numerical models of energy micropiles, undertake large-scale simulations and data-driven analysis to explore
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of methods for tasks such as data harmonisation, phenotype representation, genomic analysis and patient or gene prioritisation. Working within research high-performance computing (HPC) environments
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the field. Specialized training: Support for courses and workshops that build skills relevant to the scholar’s research and career goals, including microbiome and bioinformatics analysis, scientific and grant
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texture measurements on vegetable tissue. You will participate in data collection and analysis and collaboratively advance the development of standard operating procedures for advanced microscopy. Learning