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Field
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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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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
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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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modelling and simulation of complex systems reinforcement learning or graph neural networks proficiency in Python and related computational toolchains a strong interest in interdisciplinary research bridging
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or graph neural networks proficiency in Python and related computational toolchains a strong interest in interdisciplinary research bridging project governance, systems thinking, and intelligent control
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Shai Evra Graph theory, representation theory, number theory Adi Glucksam Complex analysis, potential theory, and dynamics Or Hershkovits Geometric analysis Mike Hochman Dynamical systems
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on “Maternal Immune Activation” involving the development of novel artificial intelligence methods (graph and geometric deep learning, LLMs, …) working on methods for predictive multi-omics integration