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PhD Scholarship in Multimodal Federated Learning and Medical Image Analysis Job No.: 695949 Location: Clayton campus Employment Type: Full-time Duration: 3-year and 3-month fixed-term appointment
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System Interferometric Reflectometry (GNSS-IR) can be combined with satellite Earth observation and land-surface modelling to produce more accurate, spatially detailed and temporally frequent soil moisture
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and apply machine learning and deep learning models to analyse complex, multi-dimensional datasets, including spatial transcriptomic, proteomic, and single-cell sequencing data. The candidate will
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with one of the following doctoral research projects. 1. AI-Aided Site Reconnaissance and Rapid Assessments for Settlement Upgrading How can spatial analytics, remote sensing, and machine learning models
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fields and control systems. In addition to this, simulations of electron spins and subsequent data analysis are also an important aspect of our work. Wide-field quantum microscopy of exotic materials High
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spatial and temporal dimensions Quantitative functional lung imaging using near field X-ray speckle Ultra-low dose Computed Tomography for lung, brain or breast imaging. Dark field X-ray imaging Translating
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fields; • experience with large datasets, spatial and environmental analysis, and field work will be viewed favourably; • proficiency in R or similar scripting languages for data cleaning, analysis and
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successful. To be eligible, you must be an Australian or New Zealand citizen or Australian permanent resident. Please see other eligibility requirements and other details at the following link. https
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characterising spread of material for future star formation. Simulation and observational analysis of star formation regions as mission prep for planned NASA SMEX mission Eos and potential Monash cubesat project
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: Experimental evaluation of biomarker behaviour under differing rehydration strategies (i.e. water-only versus electrolyte-supported rehydration). Data analysis and classification framework Data collected across