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Language Models (LLMs). Development and validation of personalized AI models for early diagnostics and risk stratification of progression risks among patients with chronic hepatitis B infection
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and summarize findings related to AI and large language models and their applications to above mentioned fields; Prepare research reports, presentations, and manuscripts for publication; and Collaborate
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computational screening, or machine learning for materials property prediction is essential. Candidates with prior experience in developing AI models for accelerated materials discovery, optimization of material
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. degree in cognitive neuroscience, neural computation and machine learning, or a related field with a strong background in EEG, functional near-infrared spectroscopy (fNIRS), or computational modeling, as
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Language Models (LLMs). Development and validation of personalized AI models for early diagnostics and risk stratification of progression risks among patients with chronic hepatitis B infection
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culture, molecular and cell biology, and animal models. Those with a Master’s degree plus at least 1-year post-qualification experience or Bachelor’s degree with 3-year post qualification may be considered
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experience in one or more of the following areas: development of methods for multi-omics data integration, application of machine learning models in life science, single cell data analysis and spatial
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studies, specifically with mouse models. Proficiency in computer skills, including Microsoft Office (e.g., Word, Excel, PowerPoint) for data management and reporting. Desirable Skills: Proficiency in
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language models and their applications to above mentioned fields; Prepare research reports, presentations, and manuscripts for publication; and Collaborate with faculty, researchers, and other team members
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methods, such as multiple linear regression, mediation analysis, multilevel modeling, and/or latent variable models. Or those with experience in managing and analyzing large datasets, and the use