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Field
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Approximation. Parameterized Complexity is a vastly growing area within theoretical computer science that allows for the development of exact and approximation algorithms for computationally hard problems by
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learning algorithm to develop an ability to choose what main data pattern/structure to preserve? This PhD project will approach this question by developing modelling strategies and pipelines to enable human
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will develop autonomous on-board guidance algorithms for space missions using open-source numerical solvers for convex optimisation developed at the University of Oxford. The focus will be on designing
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: Odense, 5230, Denmark [map ] Subject Areas: mathematical theory, algorithm development, error correction, adaptation of GBS-based algorithms to other quantum computing platforms, and the development
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formulation, which displays striking similarities to that used by the Computational Fluid Dynamics (CFD) community, has inspired the investigators to adopt conventional CFD algorithms in the novel context
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. will be developing efficient and reliable algorithms that arise in the context of topological data analysis and rendering for the visualization of large amounts of data. Close cooperation with other
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quantitative analysis skills and experience developing algorithms and/or conducting statistical analyses with biological datasets. Background and work knowledge in statistics, algorithms, optimization of novel
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deep learning and scalable deployment Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches Design and run experiments using the latest
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water, etc.) by fusing sensor data (e.g. sonar, INS, USBL). Collaborative control strategies: Developing coordinated control algorithms that allow the USV and UUV to perform joint missions without
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detailed local inspection. Digital twin simulations: Developing simulation environments replicating sensor characteristics and anomaly conditions to test perception algorithms under controlled, repeatable