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Combining models and combining data / Realistic simulation of clinical trials Developing LLMs to utilise ODEs and ProbML as tools, Code synthesis for causality Generalisability, transportability and
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• Constrained experimental design • Combining models and combining data / Realistic simulation of clinical trials • Developing LLMs to utilise ODEs and ProbML as tools, Code
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genomic variation and phenotypic traits, predict gene essentiality, and model evolutionary trajectories. The role involves using large language models as coding assistants for efficient pipeline development
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MoSS with various diffraction methods, to develop code to automate the characterisation of MoSS with diffraction and other methods. The successful candidate will be involved in the supervision of a PhD
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modelling, and advanced regression techniques, ensuring transparency and reproducibility. Use and maintain high standards of coding practice for data processing and analysis, preferably using R, including
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MoSS with various diffraction methods, to develop code to automate the characteri sation of MoSS with diffraction and other methods. The successful candidate will be involved in the supervision of a PhD
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our Purpose and Values and our Staff Code of Conduct. At Durham we actively work towards providing an environment where our staff and students can study, work and live in a community which is
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-dimensional data such as structural and/or functional neuroimaging or omics information, and/or temporal data as recorded using smartphones and/or wearable devices Excellent statistical and coding skills with
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-versa. Digital Humanities methods can broadly consist of familiarity with skills like coding, data scraping, archiving, data visualization, relational databases, or digital storytelling, but may not be
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Code of Conduct. At Durham we actively work towards providing an environment where our staff and students can study, work and live in a community which is supportive and inclusive. We welcome and