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areas: wireless security, wireless communications, AI/machine learning for communications, covert communications, signal processing, and/or RF design. Ability to conduct research independently and
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. Advanced machine learning, reinforcement learning, and agent-based optimization techniques will be developed to reduce voltage deviations, cut active power curtailment, and improve system adaptability under
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the research themes of software engineering, engineering computing, sensor networks and measurement technology, grid computing and physics data analysis, machine learning, and interactive and collaborative
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material science, digital fabrication, hardware systems, artificial intelligence, and computational design to advance smart textiles and wearable systems for various applications, including health and well
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uncertainty. Utilize machine-learning and data-mining approaches to recommend bioengineering interventions. Develop new machine-learning algorithms. Integrate machine learning techniques with mechanistic
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on integrating an agentic platform for electrocatalyst discovery and developing novel machine-learning algorithms (e.g., reinforcement learning) for materials discovery and process optimization. The candidate will
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coarse grained reconfigurable arrays (CGRAs), virtualisation of FPGAs using partial reconfiguration, and accelerator support for machine learning. Postdocs at KAUST enjoy generous salaries and free
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a new computational paradigm that combines the versatility of the digital computer with the efficiency of close-to-physics computing. The group targets the full computational stack, from materials
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. Develop AI, machine learning, optimisation, and decision-support algorithms. Build digital twin environments, physics-based modelling, concept evaluation frameworks, and technical risk assessments. Develop
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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