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Field
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Trailblazer builds new research capabilities and drives commercialisation outcomes in the resources technology and critical minerals sector. There will be opportunities to be involved in the Trailblazer Program
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analysis, contextual analysis, audio feature extraction, and machine learning models to identify and assess potentially dangerous content. Similarly, computer vision models are implemented to analyse images
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optical modelling and characterisation Optimisation of PV collector cell design How to Apply To be considered for this position, please submit your curriculum vitae and respond to the selection criteria
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, simulation environments, and rapid prototyping. About You To be suitable for this role you will need to have experience in the below key accountabilities: · Strong background in modelling and simulation
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Southern Cross University is a dynamic, ambitious institution focused on its purpose of changing lives through revolutionary learning and research with real impact. Our game changing Southern Cross Model
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accepted by the intended users due to their limited capabilities to sustain long-term interactions. In this project we propose to develop compositional vision-language models for social robots, enabling them
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modelling astrophysical phenomena. The PhD project will focus on developing theoretical methods to generate accurate data to meet this demand. The student will gain expertise in high-performance computational
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-renewable-energy-engineering Skills & Experience: A PhD in Computer Science or a related field. Thorough theoretical background in machine learning and deep learning. Demonstrated experience in developing
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Computing and Information Systems, biostatisticians in Population and Global Health, big data research in genomics in the Melbourne Integrative Genomics research hub, optimisation researchers in the ARC
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to make a meaningful impact, we encourage you to apply. About you A degree in Data Science, Statistics, Computer Science, Environmental Science, or a related field 5-8 years experience in data analysis