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Field
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candidate will work closely with experts in food allergy, neuroimmunology, gut physiology, and computational biology to characterize immune cell responses, construct spatial maps of inflammation along the gut
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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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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
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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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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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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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multivariable statistical methods. Support for skills development is provided within the Horse Microbiome Research Group and the university’s Doctoral College . Delivery of this project in collaboration with
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fundamentals Strong programming skills in at least one relevant language (e.g., Python, R, Rust, JavaScript) Experience with data analysis, statistical modeling, or machine learning techniques Familiarity with
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, and a structured approach to problem-solving. Additional qualifications Coursework or other experiences with the following subjects are valued: scientific computing, optimization, statistical machine