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accurate completion. Algorithm Design: Design and implement algorithms for tensor completion, considering the unique challenges posed by sparse and multidimensional network data. This involves developing
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, and use smart phone apps to collect passive and active data using a prospective observational cohort study design. We will use this data to develop and validate a personalised risk prediction algorithm
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Nanomaterials that detect protein-structural changes Nano-optical devices for protein-signal sensing AI algorithms for protein structure and dynamics prediction Outstanding Postdoctoral Training Strategy
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Processing and Machine Learning to develop signal processing and machine learning algorithms and methods for communication networks. Key Responsibilities: Develop signal processing and machine learning
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river system Develop, test and apply algorithms for the processing and analysis of satellite data drawing on the latest physics-based and/or data-driven techniques Contribute to work on the automation and
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an exciting opportunity to a graduate with the relevant skills and knowledge to develop and implement machine learning techniques to derive new-to-sector advanced insights from Aids to Navigation maintenance
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) uses principles from systems neuroscience to develop reliable, low-power spiking neural networks and learning algorithms for implementation in a new generation of neuromorphic hardware. Both projects
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economics Develop state-of-the-art algorithms in algorithmic game theory and fair division Design and conduct experiments to evaluate the algorithm performance Job Requirements: PhD degree in computer science
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sparse algorithms. The successful candidate will contribute to advancing secure, trustworthy, and efficient AI solutions for scientific applications. Key responsibilities include developing state
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state-of-the-art facilities to work on the following: Developing advanced path planning, search, and exploration algorithms for multi-UAVs systems in unknown and complex 3D environments. Designing