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transcriptomics) in collaboration with lab members and our collaborators. Knowledge of integrating microbiome and metabolome data is an advantage but is not required. Similarly, experience in clinical data mining
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, Japanese history (esp. early modern), historical geography, GIS, programming (esp. Python) and computer science, statistics and data science, text mining, natural language processing, demography, economics
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of agricultural in dry lands and marginal environments, animal value chain, water and energy sustainability, biorefinery and bioenergy, cultivation and conversion of micro and macroalgae, and geology and mining
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moves. Success will be measured by having published or contributed to papers in top venues (e.g., Nature Science of Learning, Computers and Education, ACM Learning at Scale, Educational Data Mining) and
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development, translational medicine, bioinformatics&data mining, and AI-enabled biomedical engineering. As a faculty member at SUSTech, you will have the opportunity to shape the future of your career while
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Experience with machine learning, data mining and data assimilation is a plus Knowledge of git, docker, kubernetes, and/or metadata is a plus Ability to work within a team Excellent interpersonal and
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Experience: Experience in advanced analytical theories, predictive modeling, data mining, and data extraction. Experience in clinical and health services research. Expertise in using various statistical
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of AI models and LLMs specifically tailored for data mining in the physical sciences and engineering Fine-tuning and evaluation of open-source LLMs (e.g., Teuken 7B, Llama 3, Mistral 7B) specifically
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drainage, cultivation, and mining. As these areas constitute major sources of carbon dioxide emissions, accurate mapping is crucial for their effective restoration and management. Peatlands are highly
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drainage, cultivation, and mining. As these areas constitute major sources of carbon dioxide emissions, accurate mapping is crucial for their effective restoration and management. Peatlands are highly