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to apply advanced AI models in areas such as catalyst design, multi-scale modeling, and spectroscopic analysis. The Research Fellow will take on a significant role in machine learning theoretical energy
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for Quantum Technologies (CQT) The Centre for Quantum Technologies (CQT) in Singapore brings together physicists, computer scientists and engineers to do basic research on quantum physics and to build devices
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Information Engineering and Media to develop Machine Learning and AI algorithms for real-world and media-related applications. The Research Associate/Research Fellow is also expected to support teaching
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models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in machine learning, computer vision, and medical
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, molecular dynamics, and machine learning, to model battery electrolyte and solid electrolyte interphase (SEI), while collaborating with experimentalists. Qualifications • Ph.D. in Computational Materials
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Responsibilities: Conduct programming and software development for graph data management. Design and implement machine learning models for optimizing graph data management. Conduct experiments and evaluations
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Requirements: A PhD degree in mathematics or related areas, with a strong background in topological data analysis (TDA) and machine learning on biomolecular data Proficiency in programming languages such as
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, engineering, finance, and health. Key Responsibilities: To perform the pioneer research in AI for climate transformation. To further develop data-driven and machine learning tasks for fighting climate changes
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conditions. The researcher will also work with team members within the consortium in generating necessary data required for developing a machine learning model for storm surge prediction. Key Responsibilities
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, Information Systems, or a related field with a strong research focus in machine learning, biometrics, or mobile computing. Proven experience in federated learning, privacy-preserving machine learning