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in the 2025 QS World University Rankings by Subjects. We are hiring a Research Fellow in Signal Processing and Machine Learning to develop signal processing and machine learning algorithms and methods
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efficient numerical methods for solving the problems, and implement the numerical algorithms to verify the theoretical results. Job Requirements: Applicants should possess a PhD degree or have satisfied all
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of marine geophysical survey and machine learning algorithms; Job Requirements: A Bachelor degree in geophysics or equivalent and A PhD degree in geophysics / geomechanics or equivalent from a recognized
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, geometric/topological/algebraic data analysis, geometric/topological deep learning, Math for AI, categorical deep learning, sheaf neural networks, Perform data preprocessing, algorithm/method/model design
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of the research group. Key Responsibilities: Conduct research in CV/ML/robotics for infrastructure monitoring and automation. Develop algorithms and/or systems for sensing, perception, and robotic applications
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HDLs. Sound knowledge of cryptographic algorithms and secure hardware design principles. Experience with ASIC design methodologies and open-source IC design tools, particularly OpenLane. Evidence of
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of the designed algorithms and systems. Help with research presentation works such as high-quality paper writing. Job Requirements: Preferably PhD’s degree in Computer Engineering, Computer Science, Electronics
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field Strong background in control theory, optimisation-based algorithms and/or machine learning Excellent verbal and written communication skills Proficiency in programming languages in Python and/or C/C
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distributed energy resources (DERs). Design & develop optimization algorithms/tools to plan the deployment of DERs such as energy storage systems (ESS), photovoltaic generations (PV), electric vehicle charging
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aquaculture (e.g., behavioral analysis, growth prediction, digital twin, computer vision.) Develop, train, and validate advanced computational models and machine learning algorithms tailored to complex datasets