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and qualified Research Fellow to join our research team on the project titled “Development of 3D Vegetation Quality and Intensity Indices”. This project specifically looks at the processing and
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biosynthesis and the downstream extraction and purification processes. Key responsibilities will include: Research: Optimise bioreactor conditions and feeding strategies for maximum PHA production and monitor
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of this technique to rotationally and vibrationally inelastic scattering processes relevant to astrophysics and astrochemistry. Recent publications on this topic are: Astronomy & Astrophysics 692, A229 (2024); Phys
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disabilities, neurodivergent people, and people of all genders, sexualities, and age groups. We are committed to fostering an inclusive and accessible recruitment process at Monash. If you need any reasonable
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Australian National University | Canberra, Australian Capital Territory | Australia | about 16 hours ago
-elliptic diffusion processes which are induced by geometric flows, with the aim of producing new and robust methods of establishing convergence results for many flow processes. The level of this appointment
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envisaged as a full-time role but we will consider part time arrangements for the right person. As part of our commitment to recruit fairly we use anonymised-selection processes until interview, offer
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that the post-holder’s development as an academic will benefit from this appointment. View the Further Particulars and Application Process Location: Oxford Salary: The successful applicant will be
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/ machine learning algorithms to support research in the IDMxS Analytics Cluster. The RF will apply/ improve machine learning algorithms to process (e.g., classify, predict) data collected by IDMxS. Help
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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