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Field
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-related multimodal processing (audio with text and/or video), audio deep learning, or a related topic. The post holder would have experience in developing new research algorithms or methods, using languages
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-related multimodal processing (audio with text and/or video), audio deep learning, or a related topic. The post holder would have experience in developing new research algorithms or methods, using languages
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workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness
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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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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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to flow/scaled flow processes. Design and synthesize green solvents (e.g., deep eutectic solvents) for efficient biomass component extraction. Collaborate on the development of hybrid physics-based/data
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Language Processing (NLP) with a focus on large language models, deep learning, and multi-modal machine learning. The researcher will work on the project KAMAL Health: Knowledge-Augmented Multi-Modal Arabic LLMs
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experience of the candidate At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and
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Fellow in EEE to contribute towards NLP-based research and associated deep learning applications. The Research Fellow/Research Associate is also expected to support teaching activities as required by
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translational medicine concepts Publication history in relevant fields (AI/ML, genetics, toxicology) Experience with deep learning frameworks and generative AI models (e.g., GANs, VAEs) Key Leadership