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methods for causal inference in observational data, is strongly preferred. Using various existing large datasets with rich information for knowledge synthetisation and triangulation over the course of the
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Experience with clinical trialsExperience with data analysis Experience with RedCAPExperience with large-scale data, such as genomics and transcriptomics dataProgramming experience, preferably in Python or RA
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quantitative and qualitative data. This project is likely to involve undertaking a systematic review; interrogating and analysing large-scale healthcare datasets; and using qualitative methods, such as
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The successful candidate will join the CritiX research group (https://www.uni.lu/snt-en/research-groups/CRITIX/ ) headed by Prof. Marcus Völp. The team focuses on critical information
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Successful candidates will be expected to have a solid background in finance and sustainable finance, research methods, strong data analytic skills, and experience working with large datasets. RMIT
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scattering, and rheometry will be conducted to understand the dynamics and interactions between ingredients in the liquid formulation. This information will be correlated with atomic force and electron
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; Midlands Graduate School Doctoral Training Partnership | Birmingham, England | United Kingdom | 3 days ago
of the research findings. Person Specification Candidates will be expected to have a first degree in Economics or proximate fields; strong analytical skills; experience in quantitative data analysis using large
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productivity and sustainability in livestock populations. In this PhD project, you will contribute to developing accurate and unbiased genomic models for large-scale within-population genomic evaluations
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discipline that combines multiple data sources along the patient pathways and develops real-world evidence to inform future clinical practice. The project will involve developing novel methods such as
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these processes using large-scale population genomic data from modern-day and prehistoric humans. The PhD position is part of the The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS