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Description Distribution estimation algorithms for abductive inference (total or partial) in dynamic domains. Structural learning of dynamic Bayesian networks with discrete and continuous variables (parametric
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models, artificial intelligence, Bayesian models, data visualization, dynamic causal models, dynamic systems models, item response theory, large language models, machine learning, mixture models
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of Biostatistics and Population Health (BPH, https://medicine.osu.edu/departments/biomedical-informatics/divisions/division-of-biostatistics-and-population-health ) in the Department of Biomedical Informatics (BMI
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. The successful candidate will lead the computational efforts of developing and applying methods for applying proteomics and genetics data collected in situ for integrative structure modeling. Critical aspects
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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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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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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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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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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