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analysis of data. Prepares appropriate and understandable representations of data such as graphs, charts, tables, statistical summaries, etc. Contributes to the preparation of scientific manuscripts by
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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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Responsibilities Research: - Conduct research in AI reasoning, semantic modeling, and knowledge graph development. - Develop and optimize graph databases for structured knowledge representation. - Apply neural
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expressions when the matrix sizes are unknown at compile-time. The project aims to address the problem using e-graphs. An e-graph is a data structure commonly used in automated theorem provers and recently
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Shai Evra Graph theory, representation theory, number theory Yoel Groman Symplectic Geometry Adi Glucksam Complex analysis, Potential theory, and Dynamics Or Hershkovits Geometric Analysis
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well as in designing coordination strategies for them. Our recent work on RL and graph neural networks (GNNs) demonstrate some of our key research directions relevant for this position. A high degree of self
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, immunofluorescence. • Experience analyzing, graphing and interpreting research results • Experience with oral and written communication of scientific results • Mentoring and leadership potential • Familiarly with
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FWO-UGent funded bioinformatics postdocs: Unveiling the significance of gene loss in plant evolution
loss patterns across diverse plant lineages Explore graph-based algorithms for multiple genome alignment and ancestral karyotype reconstruction Position 2: Evolutionary Analysis and Network
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processing, signal processing, and network resource management to enhance performance. To optimize and analyze complex 6G networks, we use AI/ML, graph theory, and optimization techniques Furthermore, our
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
metabolomics data from clinical studies. Apply deep learning models (e.g., autoencoders, variational autoencoders, graph neural networks) for biomarker discovery, disease classification, and patient