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Field
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. The successful candidate will play a pivotal role in a project centered around variational quantum algorithm in the near-term, especially on innovating advanced error mitigation or detection techniques to solve
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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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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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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. There are opportunities to broaden out into other areas such as new algorithm development, and advanced computational methodologies for integrated analyses. You will have a key role in planning, designing and executing a
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. There are opportunities to broaden out into other areas such as new algorithm development, and advanced computational methodologies for integrated analyses. You will have a key role in planning, designing and executing a
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sensors will be designed, modeled, fabricated, and fully packaged for deployment. Sensor performance will be measured in the lab and new techniques will be developed to characterize performance for inertial
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, including autonomous robotic systems for In-space and On-orbit Servicing, Assembly, and Manufacturing (ISAM and OSAM) and space logistics. Develop and implement control algorithms for distributed multi-agent
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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