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conducting solid oxide cells (E) Skills & Abilities Practical experience of applying computational techniques to the modelling of microstructure in solid oxide cell technologies (e.g. FEM, Gaussian, Bayesian
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project, you will develop machine learning models that learn from high-throughput experimental datasets to uncover structure–property relationships and guide the selection of new experiments. The datasets
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candidates with strong expertise in Bayesian methods, uncertainty quantification, and/or machine learning applied to nuclear theory. The group’s research spans a wide range of topics including nuclear
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Software Engineering, Software Project Management, Data Structures and Algorithms and Data Base Systems. The Artificial Intelligence and Data Science Programmes has modules in Natural Language Processing
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Software Engineering, Software Project Management, Data Structures and Algorithms and Data Base Systems. The Artificial Intelligence and Data Science Programmes has modules in Natural Language Processing
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Bayesian Index Tracking: optimisation by sampling School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Kostas Triantafyllopoulos, Dr Dimitrios Roxanas Application
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and neural network methods will be used to transfer diagnostic capability between structures in a population. Bayesian approaches will also be emphasised. The Research Associate will take a leading role
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-Track) Department: Medicine | School Biomed Sci - Biomedical Informatics Division of Biostatistics and Population Health (BPH, https://medicine.osu.edu/departments/biomedical-informatics/divisions
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Research Engineer/Postdoctoral Position Decision and Bayesian Computation (DBC) – Epiméthée (EPI) Laboratory Institut Pasteur, Paris | 25 rue du Docteur Roux, 75015 Paris Position Overview We
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forecasting. Familiarity with ensemble methods, Bayesian approaches, and uncertainty estimation. Experience with large-scale or messy real-world data (structured and/or unstructured). Interest in or experience