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Field
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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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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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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-cell omics datasets
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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
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above. Proficiency in scientific programming (Python) and deep learning frameworks. Fluent oral and written communication skills in English. Desired qualifications: Expertise in broader topics in
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn't imagine the future, we invent it. If you're passionate about joining a community that challenges the
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and deep learning. Strong Computer Vision background with publications in top/mid-tier conferences e.g., CVPR, ICCV, ECCV, WACV. Strong mathematical understanding and skills in applying Machine Learning
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to time by the Project Lead. We hope you will bring: Experience of undertaking research in developing and deploying ML models (including regression, classification, deep learning, multi-agent
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, empathy, and a deep understanding of diverse perspectives and cultures. About the Role As the Research Fellow: Aboriginal, you will be responsible for undertaking research projects in Aboriginal and Torres