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biological properties of LATs, including particle size, degradation rate, and drug release profiles. You will build representational methods for APIs and excipients, apply Bayesian optimisation to experimental
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prompted by the same environmental stressors across a species’ geographic range and through time. The post holder will develop a new Bayesian model, MESS, to analyse the dynamics of extirpation. The MESS
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environmental stressors across a species’ geographic range and through time. The post holder will develop a new Bayesian model, MESS, to analyse the dynamics of extirpation. The MESS model will adapt
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developing cutting-edge active-learning (Bayesian optimisation) methods that integrate chemical knowledge by capitalising on Large Language Models (LLMs) as well as human knowledge. You should have a PhD in
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across Oxford, Nanyang Technological University and National University of Singapore studying the reliability of LLMs through the lens of uncertainty quantification (UQ), Bayesian inference, conformal
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developing cutting-edge active-learning (Bayesian optimisation) methods that integrate chemical knowledge by capitalising on Large Language Models (LLMs) as well as human knowledge. You should have a PhD in
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. The PDRA will quantify the differences in calculated and measured experimental conditions by adapting the Geodetic Bayesian Inversion Software ( https://doi.org/10.1029/2018GC007585) ). Working alongside our
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) is developing ML methods supporting the experimental HT workflows and, so far, focused on intelligent design of experiments based on Bayesian Optimisation. The team at Cambridge has its own high