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UCD Center for Labor and Community - Engaged Research Fellowship Project Coordinator (PROJECT POLICY
marketing materials based on intended audience (colleagues, faculty, students, employers, etc.). Advanced computer skills in Microsoft and Mac environment, including, Access, Word, Excel, PowerPoint and
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, machine learning, and life cycle assessment, we aim to create sustainable wearable systems to enhance human well-being. For more details, please view https://www.ntu.edu.sg/mse/research . We are looking
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UCD Center for Labor and Community - Engaged Research Fellowship Project Coordinator (PROJECT POLICY
requirements, and changing priorities. Skills to prepare data presentation and marketing materials based on intended audience (colleagues, faculty, students, employers, etc.). Advanced computer skills in
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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repeated for a database of events covering different sea ice types, conditions, locations, and rates of ice deformation (from docile to violent). Machine learning techniques will then be used to find a
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areas. Key Responsibilities: To independently undertake research in computer vision and machine learning. To produce research reports and/or publications as required by the funding body
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an advanced AI-augmented digital platform (AiCT-Med) powered by cutting edge machine learning models trained on multiple large, aged care datasets from providers across Australia. The platform is designed
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Qualifications* PhD in Computer Science or Engineering, Biomedical Engineering, Neuroscience, Bioinformatics, or other relevant field. Experience with machine learning and statistical analyses. Proficiency with
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from backgrounds, including computational chemistry, bioinformatics, systems biology, physics and machine learning. The project offers a unique opportunity to collaborate closely with experimental