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Field
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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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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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Stage Researcher (R1) Positions PhD Positions Country United Kingdom Application Deadline 2 Nov 2025 - 23:59 (Europe/London) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer
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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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identify specific combinations of stresses that provide the greatest challenges to plant productivity. This position will be especially suited to candidates with an interest in plant-environment interactions
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findings, and help attract funding. PhD in Computer Science, Computational Bioengineering, Mechanical or Electrical Engineering Excellent coding skills Preferably a familiarity with machine learning and deep
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Is the Job related to staff position within a Research Infrastructure? No Offer Description The Role Are you an ambitious researcher looking for your next challenge? Do you have an established Are you