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patient samples. The Sheffield arm of the project will develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung
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with a background in Language Technology, Machine Learning, or Language Typology who are interested in automatic classification and analysis of hundreds of languages to undertake a PhD project exploring
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/SinglePageApplicationForm.aspx… Requirements Research FieldComputer scienceEducation LevelPhD or equivalent Skills/Qualifications Professional skills Experience in: Reinforcement Learning (RL), Model Predictive Control (MPC
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to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model development using Python and/or other programming languages
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Robotisation (PROMAR) group, headed by Matthias Rupp. The group develops fundamental and technological expertise in machine learning for materials science, including data-driven accelerated simulations and
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for clinical use. Generative and Predictive AI for Clinical Decision Support and Statistical Inference Develop biologically informed statistical methods and uncertainty estimation models to train deep learning
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta
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development. The successful candidate will contribute to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model
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position is funded by multiple NIH projects, e.g., https://tinyurl.co m/ysxhmujvThe overall goal is to : (1) develop inference and dynamic prediction models using a wide variety of data, including clinical