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information theory: designing and analysing error-correcting codes, establishing fundamental performance limits, and building practical decoding algorithms and architectures. Your research will sit at
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to the mathematical foundations of entropy, randomness and irreversibility, with possible directions including large deviation theory, algorithmic randomness, ergodic theory, quantum entropy, operator algebras and
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of platform data handling and payload data processing equipment; the implementation, inference, verification and validation of algorithms** on data processing hardware platforms for space applications** in
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models; Learning with limited supervision; Generalisation beyond pretraining distributions The precise research agenda will be developed together with the successful candidate and may evolve throughout
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ones for different stakeholder groups, as a basis for policy making? Are you interested in spatial optimization algorithms and uncertainty assessment? Then this PhD position at the Department of Human
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count, area distribution, inter-cluster distance) to cellular Potts model (CPM) simulations of spheroid growth and drug responses. Integrating multi-modal data streams, such as live-cell imaging, bulk and
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error-correcting codes, establishing fundamental performance limits, and building practical decoding algorithms and architectures. Your research will sit at the interface between the classical and quantum
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lead to better-behaved inverse problems. These mathematical insights will subsequently be translated into computational algorithms for inversion, uncertainty quantification and experimental design. The
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advanced algorithms for solving complex optimization problems. You will work with real-world case studies to design future-proof supply chain networks for key agricultural products in vulnerable regions
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benchtop spectroscopy experiments; data analysis, quantifying sensitivity and performance of the technology, and formulating algorithms for automated data interpretation and reporting; completing relevant