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research and teaching within The University of Queensland (UQ) School of Electrical Engineering and Computer Science (EESC). The QDA brings together academia, industry, and government to accelerate
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implementing new informatics tools and resources to enhance phenotyping performance or enable deep phenotyping through terminology/ontology, natural language processing, and machine learning. The role involves
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of Excellence for Data-Driven Discovery, applying advanced computational techniques to develop novel therapeutics. This position will work closely with researchers in the Center of Excellence for Data Driven
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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algorithms. Graph Neural Networks. The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another field
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. The most recent methodological research of the group includes algorithms for cell type deconvolution, high-resolution purification, and integration of single cell multi-omics data. This postdoctoral fellow
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suitable data models [CSC+23]. Objectives As far as the design of efficient numerical algorithms in an off-the-grid setting is concerned, the problem is challenging, since the optimization is defined in
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on stochastic Riemannian optimization algorithms, these methods still suffer from limitations in computational complexity. The post-doctoral fellow will build upon this preliminary work to investigate
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algorithms for dynamic structured data, with a particular focus on time sequences of graphs, graph signals, and time sequences on groups and manifolds. Special emphasis will be placed on non-parametric
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computational models, algorithms, or theory to probe the mechanisms of learning and memory. Ideal candidates will work across levels—from molecules and synapses to circuits and systems—and contribute