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Field
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principles that regulate host-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine learning tools. A specific focus will be put
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on this work by using molecular methods to unravel the forces that modulate mutagenesis across the genome of mutagen-exposed cancer cells. You’ll be introduced to advanced statistical and computational methods
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knowledge of the use of command line and programming (bash and R), -Strong skills in statistical analysis, -Good ability to work in a team, -Good command of English (reading, writing, speaking), -Good
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tunnel testing and statistical modelling using extreme value theory, the project will yield predictive frameworks for structural risk under rare yet damaging wind conditions. The results will support
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language (e.g., Python, R, Rust, JavaScript) Experience with data analysis, statistical modeling, or machine learning techniques Familiarity with handling large datasets (e.g., using SQL) and data pipelines
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, unit reliability analysis, and shared variance component analysis (SVCA) Create comprehensive data visualisations and perform statistical analyses to assess stability and plasticity of multisensory
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subject relevant to the proposed PhD project (such as mathematics or statistics) is our standard entry, however we place value on prior experience, enthusiasm for research, and the ability to think and work
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post; public engagement or outreach • Preferably demonstrable experience in academic writing for publication, e.g. peer reviewed paper(s) and/or report(s) • Advanced statistical software skills
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genetics, and may include expertise in one or more of the following: computational biology, statistical genetics, population genetics, biomedical informatics, complex genetics, clinical genetics
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change theory and practice, implementation science, and associated measurement and analytic techniques. The candidate is expected to help bridge these domains using validated statistical tools. Applicants