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Field
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fusion of sensor data and energy harvesting output, enabling adaptive and energy-aware data acquisition from textile-based wearable systems. Build adaptive algorithms that dynamically optimise data
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particular emphasis on LiDAR-Inertial Odometry (LIO), Visual-Inertial Odometry (VIO), and multi-sensor fusion for UAVs and other agile platforms. The Research Fellow will develop high-performance, robust
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segmentation, localization, mapping and multi-modal sensor fusion; (b) proficiency in programming languages such as Python and C++; (c) demonstrated ability to conduct independent research and contribute
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, Transformers, Python, PyTorch and/or TensorFlow, multimodal fusion); Active and semi-supervised learning; Software engineering for R&D (Git, reproducible environments, large-scale data pipelines). Exclusive
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such as PyTorch or TensorFlow, and ideally with probabilistic programming tools such as Pyro or Stan. Experience with multimodal data fusion and building efficient, scalable data pipelines for large genomic
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maritime traffic and identifying species present in the ecosystem. In this context, particular emphasis will be placed on the collection, compression, and fusion of acoustic data acquired via DAS systems
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learning analytics. In addition, the following qualifications would be considered an asset: Experience with multimodal machine learning and multimodal data fusion approaches. Experience with discourse
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development programs. Operate cold spray additive manufacturing systems, with occasional contributions to powder bed fusion and directed energy deposition programs. Collect process and property data and develop
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and real-time inference strategies for deployment on embedded platforms. C. Navigation, Mapping & Perception Lead R&D in 3D mapping, localization, sensor fusion, and risk-aware perception for complex
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encoders, multimodal fusion, and cross-attention; (ii) linguistic contextualization using large language models, namely fine-tuning for gloss-to-text and text-to-gloss translation; and (iii) systems