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advanced machine learning, with a focus on recommendation systems, graph neural networks, and multimodal AI, by designing novel models and methodologies, and leveraging large-scale structured and
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framework that leverages Large Language Models (LLMs) and other AI techniques. The role will focus on constructing, merging, and maintaining domain-specific knowledge graphs to harmonize heterogeneous data
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activities adhere to ethical guidelines and patient data privacy regulations. Stay updated on the latest advancements in medical imaging, image processing, robotics, and large language models, ensuring
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performance in real-world applications. Utilizing large-scale models and state-of-the-art learning frameworks to analyze complex data, enhance model efficiency, and improve robustness under limited-data
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analysis tools for the large data stream of HTP phenotyping for assessment of abiotic and biotic stress tolerance (e.g. determine extent of damage to a disease or functional response to humidity); also
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receive valuable experience in working with large-scale survey data and data analysis. There will be opportunities to work on publications together, as well as to contribute to other exciting projects
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-grade codebases, including data pipelines and analysis workflows. Developing and deploying AI agents and large language model–based tools for tasks such as document processing, information extraction
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optimization, and logistics sustainability. Develop AI models for large-scale data analysis to enhance carbon management. Apply time-series and spatial-temporal data mining to forecast logistics efficiency and
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an emphasis on technology, data science and the humanities. This project, led by LKCMedicine, Nanyang Technological University and will be focused on modelling climate- and behaviour-sensitive diseases such as
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. Requirements: Essential Bachelor’s degree in sciences or social sciences Experience with data collection (qualitative and quantitative) Excellent written and spoken English Good analytical and critical thinking