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institutions and expertise in secure distributed systems will be highly valued, enabling the candidate to bring transferable knowledge to address the security challenges in emerging blockchain ecosystems and
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reinforcement learning using degraded human feedbacks. Develop distributed localization approaches for multi-agent systems. Investigate the suitability of various sensors for robot navigation in human-sense
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scalable system frameworks capable of real-time data processing, distributed decision-making, and seamless integration with operational infrastructures. Design and deploy robust, secure interfaces
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Engineering, Computer Science, Applied Mathematics or equivalent. Strong background in machine learning, convex optimization, or distributed systems. Prior experience in federated learning, edge computing
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tokamak geometry on turbulent regimes and power-load distribution in double-null configurations Build-up a comprehensive database based on these simulations and analyze data Job Requirements: PhD degree in
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industry trends Qualification Education: Master’s or PhD in Data Science, Statistics, Energy Systems, Engineering, Applied Mathematics, or a related field. Experience: 3–5 years of experience in forecasting
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Part-Time Lecturer is expected to teach these postgraduate courses commencing in August 2025. The contents of each course is detailed below. The objective of the 2-week NM6604 course is to impart
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Fellow in Autonomous Systems and Control to design and implement efficient, performance‑guaranteed distributed control approaches, leveraging cutting‑edge learning algorithms and AI strategies