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for inferring genealogical structures such as ancestral recombination graphs (ARGs) and leveraging them to study heritable traits and human evolution. Applicants should hold a be near completion of a PhD/DPhil or
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well as methods for inferring genealogical structures such as ancestral recombination graphs (ARGs) and leveraging them to study heritable traits and human evolution. Applicants should hold a be near completion of
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efficiency and accuracy in link-tracing designs (e.g. Respondent driven sampling) Partial graph data collection strategies for networks (e.g. Aggregated Relational Data) Large scale models for anomaly
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administration as well as in teaching and research administration The research should focus on data mining, e.g., clustering, representation learning, causality detection and graph mining. This is part of your
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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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
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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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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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Differential Equations, and Graph Neural Networks. The objective is to measure and predict evolutionary forces and spatial cell interactions in healthy versus cancerous tissues, ultimately identifying