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into the ethical governance of Large Language Models (LLMs), as part of the prestigious Divirsibus Vis Plurima Solvo project. The position is full-time and fixed term for 41 months or to the funding end date of 30
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healthcare applications. We use large real-world complex datasets, including data extracted from electronic health records and medical images, for applications pertaining to patient diagnostics and prognostics
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experience with AI model development, including fine-tuning, applying and evaluating large language models for tasks such as information extraction, summarization, and other biomedical or clinical applications
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motivated Neuroscience postdoctoral fellow. In addition to neuroscience research experience, having familiar with machine learning/AI/ big data processing will be an asset. A major part of this PDF
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into the ethical governance of Large Language Models (LLMs), as part of the prestigious Divirsibus Vis Plurima Solvo project. The position is full-time and fixed term for 41 months or to the funding end date of 30
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planning and control concepts to organize customization in secondary education. A large groups of schools and other external partners provide the opportunity for interfacing research and implementation
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interactions help facilitate active scientific exchange and foster a good atmosphere, and therefore play a big role in our team. Learn more about us here: https://swa.cs.univie.ac.at/ Your future tasks: You
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take responsibility for the planning, execution and analysis of high-quality research, ensuring the validity and reliability of data at all times and will maintain ongoing scientific discussion with
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Stroke Research and Prevention Group of HKU (stroke.hku.hk) to assist in neuroimaging data acquisition and analysis, write-up of the results, as well as training of students Apply image analysis skills
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-matter interactions, development of automated AI/ML systems for novel materials processing and quality insurance. The lab combines additive manufacturing, large scale laser micro-processing, multiscale