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thesis shall highlight the proposed risk embedding technique into MASs path planning algorithms, enabling them to realise guaranteed, conservative, yet risk-feasible trajectories for efficient state-space
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maintenance, production efficiency, and quality control. While the benefits of ML are significant, its adoption also introduces risks such as data privacy concerns, algorithmic bias, model transparency issues
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foundation models, multi-modal learning algorithms, generative models, and large language models [1], have made seen remarkable advancements in the field of healthcare. It can lead to more accurate diagnosis
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in simulated environments and with real data on real UAVs. Defining and calculating measures for levels of trust in the developed algorithms is essential. These uncertainty-aware algorithms can self
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Deadline: 30 June 2025 Details This project aims to develop new algorithms for reinforcement learning from human feedback, to effectively solve complex reinforcement learning tasks without a predefined
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to make one appointment in the area of applied neuromorphic algorithm design. You will join the International Centre for Neuromorphic Systems - and be part of the wider pan-university community
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, and maintaining software systems to support these research projects. This includes building data pipelines, developing algorithms, and ensuring that data is stored efficiently and is accessible to all
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discipline boundaries to apply your work. We are seeking to make one appointment in the area of applied neuromorphic algorithm design. You will join the International Centre for Neuromorphic Systems - and be
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, programming languages, data structures and algorithms, operating systems, network security, visualization, and human-computer interaction, as well as participate in the full range of faculty responsibilities
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the results of which would be used to enrich the available experimental data in order to develop a Design for Manufacture and Performance concept based on machine learning algorithms where the required