10 machine-learning-"https:"-"https:"-"https:"-"UCL" Fellowship positions in Hong Kong
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- “Towards digital biomanufacturing – developing physics-informed machine learning framework for the advanced multi-modular 3D bioprinting system”. Qualifications Applicants should have: (a) a doctoral
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years of post-qualification experience at the time of application; (b) experience in using machine learning for research projects; and (c) have a good command of both written and spoken English
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and text pipelines: ASR (FunASR/Whisper), speaker/acoustic features, text cleaning/tokenisation, dialogue semantic tagging; • Computer vision and video understanding: OpenCV/ffmpeg, frame sampling
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for hepatocellular carcinoma; (b) establish secure, regulatory-compliant development platforms capable of ingesting, curating, and continuously learning from multi-center hepatocellular carcinoma datasets; (c
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training frameworks to support pre-training, post-training and reinforcement learning for Large Language Models (LLMs); (c) lead the pre-training of both Dense and MoE LLMs, optimising for performance
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. For the post of Research Assistant, applicants should have an honours degree or an equivalent qualification. For both posts, applicants should have relevant research experience in ultra-precision machining
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twelve months] Duties The appointees will assist the project leader in the research project - “A multimodal intelligence-enabled strategy learning approach for cognitive human-robot collaborative assembly
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of the project is the application of artificial intelligence in language teaching and learning. He/She will be required to: (a) conduct independent and collaborative research on individual differences and
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for a third year is subject to satisfactory performance and mutual agreement. The successful candidate will only be required to teach one course during the first two years. Funded by the Glorious Sun
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twelve months] Duties The appointees will assist the project leader in the research project - “A multimodal intelligence-enabled strategy learning approach for cognitive human-robot collaborative assembly