53 algorithm-development-"Prof"-"Prof" Fellowship positions at The University of Queensland in Australia
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the opportunity to lead impactful research in agribusiness, rural development and agricultural systems, applying systems thinking to address complex challenges affecting food systems and rural livelihoods. You’ll
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% Superannuation (Academic Level B) Based at our St Lucia Campus About This Opportunity Lead preclinical vaccine development as part of an established research group developing a Rapid Response Vaccine Pipeline
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Performance . About You The successful candidate will play a key role in the development and validation of computational tools that integrate spatial transcriptomics, algorithmic methods, and machine learning
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learning and mechanistic modelling, sensor network development, efficient data sorting and processing algorithms, real-time and model predictive control, and transformative applications in the wastewater
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modeller to help lead a collaborative research programme between the University of Queensland and King Abdulla University of Science and Technology (KAUST) with Prof David Suggett. Our shared goal is to
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-based hail climatology for France, Switzerland and Northern Italy, including hail swaths per event across more than a decade. Design, develop and train geostationary satellite-based hail algorithms using
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: Research: Development and validation of predictive maintenance algorithms for solar farms. Interface with industry partners for knowledge sharing and feedback. Play a key role in reporting to the funding
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. The QDA is focused on advancing quantum technologies to support decarbonisation and clean energy goals. The successful candidate will contribute to the development and implementation of precision atomic
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developing advanced, data-driven models and tools to integrate renewable generators securely into Renewable Energy Zones (REZs). Our goal: a reliable, affordable, and resilient decarbonised power system
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, optimise, and design experiments using lab-scale bioreactor systems. You will generate the high-quality biological datasets required to train the biological layer of a digital twin, a computational model