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tracking for microbes and microgels in phase-contrast and fluorescence images Optimise these models and pipelines for real-time performance and integrate them into an action–perception loop that drives
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following responsibilities: ● Adapt, enhance, and validate an existing bottom-up/activity-based emissions model to examine maritime shipping emissions at a global scale. See https://www.sciencedirect.com
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classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and feedback Develop active learning and
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Francophone literature, approached through methodologies developed in ecopoetics. Particular attention will be given to the contrasting literary models of utopia and dystopia, articulated in both realist and
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of Quantum Chromodynamics in scenarios characterized by multiple energy scales. The expected results will help refine phenomenological models, improve Monte Carlo simulations, and contribute to a better
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the School of Engineering and contribute to industry-engaged research in construction materials and modelling. This role offers a rare opportunity to work across laboratory experimentation, scaled-up
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viable product and using customer discovery to validate product-market fit. Work will include prototyping elements for 3D printing and preparing elements using a concrete mix. We also expect to model/use a
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 8 days ago
School and other relevant researchers at the Australian National University. In this role you will work within a team to help develop models of green iron and steel markets or other models, help develop
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, implementing, coordinating, delegating and evaluating the nursing care of patients in alignment with the professional practice model. Minimum Education Required 2-Year College Degree Degree in Nursing from a
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implement computer vision pipelines for crop monitoring, plant stress detection, and disease identification in greenhouse environments. Apply machine learning and deep learning models (semantic segmentation