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self-driven, highly motivated, creative with excellent communication skills in written and spoken English and Cantonese. Expertise and knowledge in AI deep learning model development on histology whole
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notebook Experience with regular expression natural language processing via Python Experience with machine learning models (e.g. regularization regression, random forests, ensemble methods) Experience with
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road safety analytics framework. The role involves integrating multi-source transport datasets, developing advanced analytical and machine learning models for risk identification, and supporting
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, data-driven road safety analytics framework. The role involves integrating multi-source transport datasets, developing advanced analytical and machine learning models for risk identification, and
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of machine learning to the practical tools of deep learning, now available through modern foundation models. For the theory part, the selected candidate will work in close collaboration with
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Feature engineering and feature store development CI/CD for machine learning workflows Monitoring, maintenance, and retraining of production ML models Collaboration with data scientists to operationalize
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model training and finetuning. Job Requirements: Preferably Master in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical, proactive and a team
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Artificial intelligence and machine learning methods for model discovery in the social sciences
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Develop machine learning methods and tools with a specific focus on: Data-Centric AI: Including data attribution, data curation, and privacy preservation for large foundation models (e.g., LLMs and VLMs
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 3 hours ago
months of the appointment commencement) and experience in statistics, data science, machine learning, bioinformatics, quantitative biology, or closely related fields. Candidates must possess strong