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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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, with a special focus on RNA structure and bioinformatics. This position will focus on the development of methods and algorithms for the detection of structured non-coding RNAs. Your future tasks: We
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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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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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-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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) prior to taking up the appointment. The research requires experience in continuous-wave and pulse ESR spectroscopy, including experimental setup, setup optimisation and coding for data analysis and
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surveys, early universe and gravitational physics. Good programming experience, an enthusiasm for coding and data analysis, and the ability to work in a large collaboration, are particularly relevant
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history or ethnographic skills to this project. While not required to have Digital Humanities methods (eg. coding, archiving, data scraping, data visualisation, digital story-telling), they should be open