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view https://www.ntu.edu.sg/sc3dp/ We are looking for a highly motivated Research Fellow to join our multidisciplinary team aiming to optimize the silica glass additive manufacturing parameters and
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graph optimization, moving horizon estimation) with learning-enhanced components, including meta-learning approaches for adaptive and generalizable estimation. The candidate will be responsible for both
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and optimizing 3D printer hardware and firmware to meet experimental requirements, and integrating these systems into functional laboratory platforms. The successful candidate will work closely with
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project). Job Requirements • A strong foundation in mathematics, particularly in probability and optimization. • A PhD degree (or close to PhD completion) in engineering or management science degree
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focus on simulation, prototyping, lab testing and developing smart connectors for modular floating breakwaters. Key Responsibilities: Design and build connector models Simulation and optimization
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single‑photon performance. - Run theoretical simulations (FDTD or equivalent) to optimize structures. - Publish results, support grant deliverables, and collaborate/mentor within the team. Job Requirements
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algorithms and system frameworks that optimize cost, performance, and scalability. The role focuses on leveraging machine learning and reinforcement learning to enhance storage and service efficiency under
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one of the central challenges in quantum machine learning: how to train quantum systems efficiently and reliably on scaling quantum hardware. The research will develop novel optimization frameworks
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, and learning capabilities Develop and optimize data-driven algorithms for anomaly detection and incident response Implement scalable, modular, and containerized solutions compatible with platforms
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of samples. Prepare, mount, and align samples for CT scanning. Optimize scan parameters (voltage, current, exposure time, resolution) to achieve required image quality. Perform system calibrations and routine