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are seeking a highly motivated PhD candidate to develop efficient on-device generative AI systems based on large language models (LLMs). The project focuses on creating compact, low-latency, and energy
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project will develop novel methods for modelling and controlling large gossamer satellites (LGSs), so that they can be reliably utilised in space-based solar power (SBSP) applications. The candidate will
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ability to interpret student needs, provide individualized service, and have effective written and verbal communication skills. In addition, the ideal candidate has basic office, accounting, and computer
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is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture. The ongoing societal transformation and large green investments in
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for operational purposes, carrying out large-scale computing online is a huge challenge. Thus, we proposed a practical approach that integrates multiphysics information derived from large-scale computing, data
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neurons in the brain gives rise to the plethora of animal behaviors. Our research spans a large range of scales from the nano-scale imaging of brain circuitry, to large-scale functional imaging of brain
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robots -Distributed task planning for collaborative missions -Behaviour tree based multi agent collaboration -Reactive task allocation in large scale missions For further information about a specific
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. -Mission-specific modules such as autonomous firefighting, sampling, rescue operations, or large-scale mission planning. For further information about a specific subject see general curricula for the Board
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want to contribute to the next level of molecular computing? Are you excited about the application of AI tools to train molecular systems how to process information? Then join our team as a PhD candidate
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
generate vast amounts of operational and maintenance data, much of it remains fragmented and underutilized. Unlocking insights from this unstructured data could enable earlier fault detection, improved