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dataset, GroupLens Research. grouplens.org [8] Statlog German Credit Data, UCI Machine Learning Repository. archive.ics.uci.edu [9] ERASER Benchmark, ERASER Project. eraserbenchmark.com [10] e-SNLI Dataset
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managing and curating large datasets and with machine learning techniques preferred. Excellent oral and written communication skills and the ability to perform both self-directed and guided research
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standards and data protection regulations. This includes managing sensitive data correctly and guaranteeing that machine learning applications are developed with ethical considerations in mind. Participate in
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candidate will be at the forefront of integrating advanced optical technologies with machine learning techniques to develop novel, high-performance fibre-optic sensing applications. You will be responsible
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Research Fellow in AI for Healthcare (1 FTE) – ArcHub ICUSafeNotes-School of Computer Science 011287
breakthroughs into commercial healthcare solutions. In the ICUSafeNotes project, we are conducting research and development of multimodal machine learning models that includes ICU note summarisation and analysis
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optimization of multi-modal LLMs. Investigate and implement methodologies to ensure AI authenticity, accountability, and the integrity of digital content. Develop and refine machine learning and deep learning
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devices. Minimum requirements: Advanced knowledge of machine learning models and Python tools for signal processing and machine learning. General knowledge of system architecture and APIs. Previous
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in Social Work, Psychology, Public Health, or a related field. Must have defended dissertation before starting the fellowship. Must be within three years of completing their doctorate. Computer Skills
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health monitoring, preferably with a publication record in top-tier journals; and (c) be proficient in mainstream research frameworks for deep learning and computer vision. Applicants are invited
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, Computer Science, Electronics Engineering or equivalent. Experience in one or more of the following areas: machine learning, deep learning, software-hardware co-design, computer system performance, design