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Field
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We are looking for a Research Fellow to conduct the research for the project entitled “Manual Assembly Job Quality Inspection”. The role will focus on research and development of AI algorithms
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antennas to immediately transmit the information to a medical doctor when urgent action is required. However, such batteries need to be completely safe and non-toxic, as well as energy dense and lightweight
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learning algorithms. We combine statistical methods with online reinforcement learning algorithms to develop reinforcement learning algorithms and inferential tools. The successful applicant will be expected
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work with hands-on offloading algorithm design and development for IoT networks. The core responsibility is to build and validate edge-assisted offloading strategies, complete with software APIs, through
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and optimisation algorithms, focusing on their practical application in the context of the RaceEngineerAI project. Tasks include: - Developing models capable of simulating the behaviour of racing
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learning algorithms (Deep learning, Reinforcement learning, etc.); Proficiency in written and spoken English - essential for data analysis and communication with stakeholders Excellent oral communication
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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), Sloan Digital Sky Survey (SDSS), Antarctic Impulsive Transient Antenna (ANITA), Payload for Ultrahigh Energy Observations (PUEO), Askaryan Radio Array (ARA), IceCube/IceCube-Gen2, and the All-Sky
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, train, and validate advanced computational models and machine learning algorithms tailored to complex datasets. Collaborate with multidisciplinary teams including biologists, engineers, and clinicians
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems