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at the University of Adelaide, contributing to cutting-edge research in computer vision and machine learning for space applications. This role focuses on advancing machine learning and computer vision research, with
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machine learning to create improved reconstructions of the ice sheet. This role is based in Hobart, Tasmania and visa sponsorship and relocation allowances may be considered for the right candidate. What
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collaboratively with colleagues, strong computer literacy with proficiency in Microsoft Office and relevant university systems, and the ability to learn new software as required. Experience working with schools
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Performance . About You The successful candidate will play a key role in the development and validation of computational tools that integrate spatial transcriptomics, algorithmic methods, and machine learning
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geometry, and/or data science. Specific topics of focus include, but are not limited to, linear response, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics
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trials. In this role, you will lead the design, development, and deployment of a real-time clinical AI evaluation platform across Australia, while building your research profile in machine learning with
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with verifiable experience in longitudinal data analyses methods, structural equation modelling, network analysis, machine learning and/or adaptive trial design. Essential are outstanding academic
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immunology, including ELISA, flow cytometry, ELIspot Molecular biology expertise, including RNA extraction and RT-qPCR. High-level computer and database literacy skills, including the Microsoft Office suite
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providing lifelong learning, skills, and research locally, regionally, and globally. With University and TAFE campuses in Ballarat, Berwick, Gippsland, and the Wimmera we are positioned to provide pathways
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ternary neural networks using FPGA devices. The successful candidate will have significant experience in machine learning, FPGA design and an outstanding track record in conducting machine learning research