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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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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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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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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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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
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DTU Physics seeks a talented and motivated postdoctoral researcher for a 2-year position in computational nanophotonics. The goal is to use inverse design, such as topology optimization