16 machine-learning-modeling-"https:" Fellowship positions at Nanyang Technological University
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background in geomodelling, geophysical and geotechnical investigation, geomechanical engineering, and machine learning. You will be expected to work effectively on a geophysical/geomechanical project, to
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learning theory. Hands-on experience with machine learning or deep learning models for finance or economics. Excellent written and oral communication skills. Ability to work independently, manage deadlines
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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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optimisation techniques, machine-learning methods and data-driven modelling. Familiarity with robotics and autonomous-system simulation environments such as ROS/ROS2, Gazebo, AirSim, Unity, or equivalent
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, or a related field Strong background in computational modelling of surface chemistry and oxide materials Experience in developing and training interatomic machine learning potentials Experience in
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support the development of AI-driven and data-driven approaches for the discovery and design of functional materials. The role will involve the development and application of machine learning models, high
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-driven and machine-learning models. The role will also involve conducting materials synthesis and electrochemical studies, troubleshooting and improving automated workflows, analyzing research data, and
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at NTU are looking for a Research Fellow (RF) to carry out research in probabilistic machine learning and GenAI, by exploring cutting-edge approaches such as sequence model design, continual learning
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) 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, Franka Emika
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Osmosis (NF/RO) Membrane for Industrial Separation and Purification Application” Key Responsibilities: Integrating Machine Learning (ML) with Molecular Dynamics (MD) to predict phase-separation dynamics and