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AI-modeling and coding. The successful candidate will work alongside a multidisciplinary team, leveraging artificial intelligence and computational methodologies to optimize treatment plans, enhance
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of relevant training and experience, experience conducting literature searches, running code, and managing both numeric and sequence data in a research-intensive environment, experience with at least one data
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people of all abilities, cultures, sexual orientation, and genders. Pre-employment safety and suitability checks: In accordance with the National Higher Education Code to Prevent and Respond to Gender
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discrete dislocation plasticity modelling of metals and a good knowledge of dislocation plasticity and solid mechanics. Fortran coding experience and knowledge of Abaqus, Fortran user subroutines and crystal
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experience managing and analyzing complex datasets using R, Stata and/or SAS. Preference will be given to candidates with proficiency coding and managing multiple large datasets and experience estimating
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coding knowledge as these are transferrable to R. The candidate will be working with the Principal Investigator(s) on the analysis of national health datasets, utilising an array of methods to infer
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, bacterial cell culture, biochemistry assays, pipetting, autoclaving, coding, organic synthesis, NMR and IR spectroscopy, and computational data analysis. This engagement is scheduled for one-year in duration
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model validation. Contribute to reproducible modelling workflows (version control, documentation, shareable code and outputs) and participate in production of open research outputs. Support and lead
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, computer science, statistical or population genetics, or a related discipline), and a strong motivation to work on problems in genetics and you will also have relevant coding experience with producing high-quality
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, contributing to open and reproducible workflows (code, metadata and documentation) in line with project and UKRI expectations. Prepare research outputs including papers, conference presentations and