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Field
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: graph neural networks, natural language processing, algorithmic learning, fault-tolerance, blockchains, consensus, cryptocurrencies, digital money, central bank digital currency, decentralized finance
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multimodal fusion, robust pose estimation, and real-time processing. Together, these roles form a cohesive effort to bridge algorithmic innovation with practical applications in next-generation media
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adaptability, and safety; Applying AI and optimisation techniques (e.g. reinforcement learning and evolutionary algorithms) to adapt locomotion strategies to varying surface conditions; Supporting
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advanced algorithms that efficiently utilize UWB signal features (RSSI, channel impulse response, phase and amplitude data, Doppler maps,..) to support both high-precision localization and radar-based
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intelligence, deep learning, media data processing including images and videos, etc., including their applied fields) B) Optimization and related fields (optimization algorithms, mathematical optimization
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: build a digital twin in Isaac Sim/Lab for the robot cell; mirror the real setup with matching kinematics, collision geometries and dynamics Algorithm Implementation: Develop and deploy reinforcement
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) into communication channels to defeat matching algorithms, presentation attack detection, or human reviewers. Protections against these attacks focus on two areas: defending against the injection and detecting
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sciences, who ad- dress key issues in AI such as reproducibility, safety, trustworthiness and robustness, and who engage with the theoretical and algorithmic foundations of AI. A strong commitment to
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of distributed computing, machine learning, image and text analysis, randomized data structures, high-performance computing, and quantum algorithms. Beyond this research, we aim to support computational thinking
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. Without these guarantees, the algorithms will remain limited to experimental testbeds. The aim of this project is to address this limitation by combining the complexity of deep-learning control policies