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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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of Tokyo and Associate Professor Atsushi Nitanda from A*STAR. Key Responsibilities: Research & Development Conduct original research in deep learning, related to uncertainty quantification for large models
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. Strong programming skills in Python, with proficiency in deep learning frameworks such as PyTorch, TensorFlow, or JAX.. We regret that only shortlisted candidates will be notified. Hiring Institution: NTU
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Engineering in the 2025 QS World University Rankings by Subjects. The EEE Rapid-Rich Object SEarch (ROSE) Lab focuses on research in: (i) visual search & retrieval, (ii) video analytics & deep learning, and
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The project is to be carried out in collaboration with Schaeffler to study “collision monitoring and control system of cobot based on fiber optic sensing and deep learning”. Research Assistant
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, such as, geometric/topological/algebraic data analysis, geometric/topological deep learning, Math for AI, categorical deep learning, sheaf neural networks, PINN/KAN models, neural operators, etc, and
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to diverse academic and industrial audiences. Proficiency in Python and deep learning frameworks such as PyTorch. Experience with Linux environments and GPU cluster management is essential. Competent in
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research, formulate hypotheses, and design effective experimental plans. Strong programming skills with deep learning frameworks (e.g., PyTorch). We regret that only shortlisted candidates will be notified
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The candidate will be expected to work on a project in collaboration with Schaeffler to conduct research on “collision monitoring and control system of cobot based on fiber optic sensing and deep
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scientific leaders and researchers. Job responsibilities The project aims to advance the use of machine learning techniques to model and understand plasma turbulence in magnetically confined fusion plasmas