10 machine-learning-"https:"-"https:"-"https:"-"https:"-"Iscte-IUL" Fellowship positions at Hong Kong Polytechnic University in Hong Kong
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and energy materials. Preference will be given to those with knowledge of computer programming, AI and/or machining learning. Applicants are invited to contact Prof. Jianguo Lin at telephone number 2766
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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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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