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Field
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to the Terascale Project Knowledge of research methods relevant to AI for materials science Knowledge of one or more of the following areas: machine learning interatomic potentials, generative AI, automated and
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robots, robotic manipulators, or locomotion systems. Knowledge of machine learning, reinforcement learning, imitation learning, or computer vision techniques for robotics applications. Strong analytical
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programming languages. Experience with DICOM data, medical-image registration, high-performance computing, or GPU-based computation. Familiarity with machine-learning or deep-learning methods for medical-image
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publications at major machine learning, AI or closely related conferences and journals; and (b) good communication and writing skills in English. Applicants are invited to contact Prof. Qiu Anqi via email at
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research in various areas of mobile network systems, multimedia and AR/VR/XR systems, robotics and machine learning, focusing on fundamental aspects as well as on applications in multidisciplinary contexts
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submitted a relevant PhD and have expertise in behavioural science, AI, machine learning and/or an intersection of those fields. This expertise should be explained in a letter of application and demonstrated
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on future long autonomous navigation techniques. About you a PhD in a relevant field or near completion extensive knowledge of robotic navigation, machine learning or other relevant fields experience working
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researcher in Computer Science, Data Science, Human-Computer Interaction, Psychology, Empirical Educational Research, Learning Sciences, Cognitive Science, or related disciplines who is eager to contribute
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of future reactor systems with a focus on systems relevant for Norway. The objective is to further develop and validate machine-learning surrogate models derived from high-fidelity multiphysics simulations
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . We are looking for a Research Fellow in Multi-Agent RL for Autonomous Drone Swarm to develop learning-based