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Field
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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks) Proved experience in working independently and as part of a
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, computational fluid dynamics and material science, dynamical systems, numerical analysis, stochastic problems and stochastic analysis, graph theory and applications, mathematical biology, financial mathematics
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graph theory. Qualifications Candidates with a Ph.D. in any area of cognitive neuroscience broadly defined (e.g., Psychology, Neuroscience, Computer Science, or a related field) are welcome to apply
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causal inference methods (e.g., directed acyclic graphs) would be an asset Fluent in reading, writing, and speaking scientific English Required Skills : Experience in data management and analysis of cohort
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independently. Must be able to organize and manage a varied range of assignments and projects with high efficiency. Able to work with database, graphing, word processing, and statistical computer programs such as
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structural and algorithmic graph theory. The purpose of the role is to contribute to the project “Algorithmic meta-classifications for graph containment”, working with Professor Matthew Johnson, Dr Barnaby
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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view
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investigator and Postdoctoral scholars/fellows on the status of research. Collect and log laboratory results, clinical outcomes and/or survey data. Evaluate and perform data analysis using graphs, charts
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preliminary analysis of the data using graphs, charts or tables to highlight the key points of the research results collected in accordance with the research protocols as stipulated. Prepare and present