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Field
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design 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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the time of the appointment start date. Demonstrated expertise in current deep learning techniques (especially GNNs and/or RL) applied to biological data. Experience with spatial transcriptomics or single
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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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SIT's mission is centred on nurturing industry-ready graduates who possess deep technical expertise and transferable skills to address future challenges. We collaborate with industry in our
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program, providing them with a deep understanding of the field and hands-on experience in informatics research. In addition to the primary informatics and research responsibilities, there is the potential