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experience and expertise in web-scale data curation, development of large language models (LLMs), and in-depth LLM evaluation. LTG has a strong commitment to open-source resource and software development
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cutting-edge theories, methods, and computational tools for integrating large-scale, heterogeneous biomedical data across multi-institutional research networks, with a focus on the analytical and
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, including hybrid physics-informed approaches, to improve the estimation, monitoring and prediction of hydrological variables. Big data integration: Process and fuse multi-terabyte datasets, including
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to support personalized lifestyle interventions and clinical decision-making for chronic disease management. Perform large-scale data integration and analysis, ensuring data quality, security, and compliance
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-performance computing (HPC) environments for large-scale data analysis, including workload managers such as SLURM. For full details about this role, and other criteria, please review the Position Description
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studies. • Plan and execute experiments; analyse and interpret research findings and large-scale biological data (e.g., genomic, transcriptomic, microbiome) and familiarity with following analyses – sc
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. Perform data management, large-scale statistical analyses, and ensure high standards of documentation and reproducibility. Work effectively with colleagues across the CTP and with local and international
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the research project - “Large language models for computational social science and FinTech applications”. He/She will be required to: (a) study and investigate the fundamental problem for identifying
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, including hybrid physics-informed approaches, to improve the estimation, monitoring and prediction of hydrological variables. Big data integration: Process and fuse multi-terabyte datasets, including
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large administrative or claims data (e.g., Medicaid, commercial claims, or similar) Clear scientific writing and communication; a track record of publications Special Instructions Please include: · CV