13 algorithm-sensor-"DIFFER" Fellowship research jobs at The University of Queensland
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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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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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your discipline. This is your chance to lay the foundation for a successful academic career while contributing to innovative projects that make a real difference. At Level B, as a Research Fellow, you’ll
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, and impact. You’ll have access to world-class facilities, industry connections, and professional development opportunities to accelerate your career and make a real-world difference. Join us and
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systems for sensor alignment, motion control, and structural stability; collaborating on the assembly, integration, and optimisation of laser imaging platforms; conducting testing and validation to ensure
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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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difference and creating lasting, meaningful impact to the lives of people in our community, this is your chance! In this role, you will support the Medical Research Future Fund project “Overcoming Systemic
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at different scales (plate to flask to lab bioreactors). Optimise cultivation parameters (media composition, induction, process controls). Collaborate with strain engineering and omics teams to link genotype
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of classical and hybrid classical-quantum algorithms for treating the correlations. This position offers exciting opportunities for collaboration within UQ, across the QDA network, and with external research