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multiple ship classes and shore power applications. The project seeks to deliver industry-relevant modelling toolkits that enable optimal design and operation of greener vessels, backed by real-world
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Assistant (RA) or a Postdoctoral Research Associate (PDRA). The appointed candidates will support advanced research initiatives focusing on systems design, distributed systems, and algorithmic optimization
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optimization of large language models (LLMs) and related architectures for generative tasks, continuous learning, indexing or retrieval, support of retrieval augmented generation over many data points from long
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optimization; physical tag design compatible with its installation requirements (the leg of a bird); prototype tag building; and development of a simple server side system for data aggregation. Documenting
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modelling toolkits that enable optimal design and operation of greener vessels, backed by real-world demonstrations. The successful candidate will work at the intersection of multi-disciplinary modelling
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, to target the patient’s specific condition and obtain an optimal long term outcome. FEM coupled with Design of Experiment (DoE) will guide the design of miniplates. Ad-hoc tuning of published
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learning experiences within real-world environments. Through strategic partnerships forged by our faculty with industry, learners bridge theoretical knowledge with practical application in and out