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Field
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for complex scientific problems Designing algorithms to improve the performance of scientific applications Researching digital and post-digital computer architectures for science Developing and advancing
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implementation of algorithms to analyze the data and integrate with other data sets including clinical outcomes data. This person will also help with the generation of tools needed for manipulating and preparing
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Research Fellow in AI for Healthcare (1 FTE) – ArcHub ICUSafeNotes-School of Computer Science 011287
addressing important data-driven applications in medicine, engineering, and the physical sciences, on the basis that important applications foster novel algorithms which in turn enable new applications
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and planning algorithm for high-speed autonomous vehicle. The work will involve algorithm development, simulation, test rig set-up, and experimental validation. Requirements: The candidate must at least
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-efficient computing Developing mathematical modeling for complex scientific problems Designing algorithms to improve the performance of scientific applications Researching digital and post-digital computer
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on complex system subject to various constraints. Design and develop real-time algorithms. Simulate and evaluate system performance. Document research outcome to publish at international conference / journal
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advances the mathematical foundations, algorithms, and real-world applications of epistemic uncertainty in machine learning, with a strong focus on imprecise probabilities, uncertainty representation and
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-modal fusion techniques for localization and mapping; (b) develop and implement algorithms for autonomous robots; (c) design and execute experiments to validate and refine algorithms and systems; (d
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Responsibilities: Conduct in-depth research in sublinear time and learning augmented algorithms. Design, develop, and implement novel algorithms and models. Publish research findings in leading international
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are in particular targeting development of data-driven high-performance computing techniques for unbiased discovery of generative models & theory and algorithms for network inference with special reference