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models, focusing on industrial image analysis Develop advanced deep learning methods for power battery inspection models Design and implement novel algorithms for AI-based CT imaging Lead experimentation
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communication skills. Proficiency in developing deep learning models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in
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University. Key Responsibilities: Conduct research on methods and approaches to support air traffic management studies and applications. Develop, test, and evaluate analytical models, algorithms, and tools
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focused on using advanced numerical methods to explore low energy dynamics in strongly interacting quantum spin systems. The candidate will develop and implement advanced algorithms to investigate
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opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT. The Future Ship and System Design (FSSD) programme aims to develop
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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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. Characterise and optimise the algorithm for signal data processing for real-time cell analysis and sorting Develop a user-interface platform for end-user testing of clinical samples. Support lab procurement and
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. The Research Fellow will be employed and based at NTU, and is expected to travel overseas to collaborate with scientific teams at CEA, France. The holder will: Apply advanced machine learning algorithms
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power system simulation Energy management system (EMS) or supervisory control algorithm development Hardware-in-the-loop (HIL) platforms (e.g., OPAL-RT, Typhoon HIL) Experience in battery energy storage
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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