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the research on Integrated Sensing and Communications (ISAC) with Reconfigurable Intelligent Metasurfaces (RIS). Key Responsibilities: Design the algorithm for Integrated Sensing and Communications
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for autonomous vehicle control and teleoperation applications. Expertise in the areas of vehicle dynamics, vehicle control, localization, computer vision, sensor fusion and estimation algorithms is desired
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machine learning by designing and developing innovative models and algorithms. Key Responsibilities: Designing and conducting comprehensive research in artificial intelligence and machine learning, with a
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algorithms Perform data analysis, test and debug software Develop, implement and execute plans and tests Design and apply basic data sources and security practices Employ source code control techniques Assist
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Learning algorithms (clustering algorithms (K-means), genetic algorithms, and reinforcement learning) and end-user piloting of software systems – including technical attributes and legal/commercial
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learning, algorithms, and programming. Prior exposure to reinforcement learning or human-robot interaction is highly desirable, though motivated candidates with a strong grounding in AI/ML and willingness
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algorithms as well as deep learning workflows on GPU servers (use of Git, Docker, and PyTorch) Design, implementation, and evaluation of spatial proteomics and multiplex analyses for characterizing the tumor
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/26. Preferred Qualifications: Strong background in algorithms, human-computer interaction (HCI), and software engineering. Experience in robotics systems, interactive or intelligent systems, or AI
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in underground facilities. The project aims to evaluate sensor technologies, design and optimize multi-sensor monitoring networks, and develop advanced detection and localization algorithms adapted
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is to design a data architecture capable of harmonising sensor data and integrating them with artificial intelligence algorithms for predictive irrigation management. The research will include