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Job Description Job Alerts Link Apply now Research Fellow in Quantum Algorithms University-Level Unit: Centre for Quantum Technologies Faculty/Department-Level Unit: Computer Science Group Employee
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) on the project “Meta-Learning of Robust State Estimation for Agile Mobile Robots.” The position focuses on advancing state estimation and odometry for robotic systems, with particular emphasis on LiDAR-Inertial
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. The successful candidate will contribute to AI-powered analyses of single-cell and spatial omics to advance precision medicine. Dr Chen’s research team develops AI algorithms for spatial and single-cell omics
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and navigation algorithms for robot in complex environments. Key Responsibilities: Responsible for development of robust and reliable sensor fusion algorithms for localization and navigation algorithms
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different approaches. • Construct mathematical models that capture meaning problem formulations, and identify why existing methods are insufficient. • Develop solutions in the form of algorithm design
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to develop an online framework for assessing fatigue, structural integrity, and operability of multiple floating offshore wind turbines, supported by computationally efficient learning algorithms with
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optimisation, algorithm design and high-performance computing, with application to airport innovation. Successful candidates will join an active group of Principal Investigators and researchers to work within
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exploration and relevant disciplinaries Key Responsibilities: Develop novel imaging algorithms and validate them through synthetic experiments Extend these methods to real-world experiments to address practical
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The Research Fellow will: Lead GBD research projects, which may include systematic reviews, meta-analyses, and epidemiological disease modelling. Set research direction and oversee the design, execution, and
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progress. Ability and willingness to work some flexible hours. Extensive experience in large-scale pre-training of large language model. Experienced in developing machine learning algorithms and large