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(e.g., ROS, MoveIt) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic arms (e.g., UR5
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Proficiency in Cadence Proficiency in Python and Matlab Experience in implementing algorithms for machine learning Good written and oral communication skills We regret to inform you that only shortlisted
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work environment Learn more about CQT at https://www.cqt.sg/ Roles & Responsibilities Administrate CQT PhD students candidature milestones (eg. QEI, QE II, Progress Report, Oral Defence etc) Administrate
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(DFT) simulations and develop machine learning potentials to investigate zeolite-related systems. The role will focus on delivering research projects and promoting research excellence in this area. The
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, image processing, machine learning, and spatial optimization to reconstruct cable surface alignments and geographic coordinates. Validate inferred routes against reference or field observations and
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platforms, including robotic systems, laboratory automation, and AI/machine learning-assisted approaches for experimental design, optimization, and materials discovery. The research will target advanced
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and predictions. Use cutting-edge methodologies such as A.I. and machine-learning to accelerate the numerical simulation processes. Develop optimal control strategies for energy systems Publish high
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electrochemical and CO2 removal research. Electrochemical process on interface phenomena MOF synthesis, testing under different conditions Simulation of scaled up process. Interface with machine learning group
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offers a friendly and international work environment Learn more about CQT at https://www.cqt.sg/ The research fellow will work closely with PI Patrick Rebentrost on developing novel quantum algorithms
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machine learning and AI acceleration. Perform performance, power, and area (PPA) analysis of processor and accelerator designs. Publish research findings in top-tier conferences and journals and contribute