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machine learning. The position will involve working with different research groups in the Department of Computer Science at the University of Cambridge, UK. In this collaborative project, we will apply
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optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware
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embedding graph-based problems, particularly those known to be challenging for classical computing architectures. Some of your responsibilities will include: Design and develop mixed-signal circuits
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shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational
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work will focus on identifying the mathematical knowledge and properties to guide hardware optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g
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. Your work will focus on identifying the mathematical knowledge and properties to guide hardware optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g
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graphs for heterogeneous pavement engineering knowledge aiming to speed up the learning cycle and support innovation and asset management. Job description The increasing accessibility of data in
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Apply and develop advanced multimodal data tools and knowledge graphs for heterogeneous pavement engineering knowledge aiming to speed up the learning cycle and support innovation and asset
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-analytical workflows, turning geodata into new answer maps. We use knowledge graphs to model these transformations and apply AI methods to scale them across large map repositories, enabling users to explore
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for the environments they manage and their level of expertise because actions may be from different types and/or at different level (technical vs. organizational for example). This requires a solution to provide