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What you'll receive The CSIRO Industry PhD Program (iPhD) aims to produce the next generation of innovation leaders with the skills to work at the interface of research and industry in Australia
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Status: Open Applications open: 25/11/2024 Applications accepted at any time View printable version [.pdf] About this scholarship Description/Applicant information The CSIRO Industry PhD Program
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algorithms for resource-efficient hydroponics and evidence-based frameworks for integrating green space exposure for improved student wellbeing. This project will potentially enhance urban food resilience
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Charles Sturt University | Charles Sturt University, New South Wales | Australia | about 3 hours ago
The file you have requested has been archived and as a result can no longer be viewed This may be because of the following: The resource no longer exists (it has been renamed, deleted or moved elsewhere). The resource has been archived. Needing to locate similar resources? Use the Search in the...
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checks (allow for between 4 to 8 weeks) and complete any other CSIRO requirements. Selection criteria To be eligible applicants must: Have a basic understanding of machine learning algorithms and deep
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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
31.07.2025, Wissenschaftliches Personal The Chair for Efficient Algorithms, led by Prof. Stephen Kobourov, is inviting applications for a fully funded PhD position at the Technical University
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algorithmic aspects of cheminformatics. The position is founded by the Challenge Programme of the Novo Nordisk Foundation: “Mathematical Modelling for Microbial Community Induced Metabolic Diseases ”, led by
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simulation of scenarios with different materials and geometries. - Support the development and implementation of signal and image processing algorithms, including fast inversion techniques, FFT, and nonlinear
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depletion, toxic algae, and pollutants. This natural sensitivity makes them powerful bio-sensors for environmental monitoring, capable of providing early warnings of ecosystem stress. However, harnessing this