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demonstrated contributions to publications in top tier conferences and journals in data mining/machine learning, Artificial Intelligence in Education and Educational Data Mining, Human-Computer Interaction
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Join a leading cancer research institute in Adelaide, Australia. We are seeking outstanding scientists for two exciting opportunities: Structural Biologist – Dr Qi Zhang’s Lab (https://www.qizhang-lab.com/ ) • Focus: Structure/function of chromatin-associated complexes using cryo-EM and...
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interpretation and faster decisions for families. This work is already underway in our lab. We’re assembling a strike team of three exceptional postdoc researchers to design autonomous discovery systems
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integration process for packaging integrated photonics devices Software Development: Create sophisticated software solutions for instrument control, data management, and analytics Research and Collaboration
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stakeholders. This will be one of a potential six postdoc hires in the research group for 2025. At ANSTO, you will have the opportunity to work closely with both the Nuclear Materials Research and Technology
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position, available until December 2027. Flexible work arrangements can be negotiated with the right candidate. Be part of the Australian Institute for Machine Learning – the largest computer vision and
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Institute for Machine Learning – the largest computer vision and machine learning research group in Australia – and contribute to world-leading research projects at the Centre for Augmented Reasoning
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microcombs that reach down to the atomic transition wavelengths for the direct readout of the clock signal. Characterising the optical clock when using the above mentioned and demonstrated narrow linewidth
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interdisciplinary research efforts process and analyse functional neuroimaging data (fMRI, EEG, etc.) to extract meaningful insights into brain function. About you a PhD in Neuroscience, Computational Neuroscience
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Postdoctoral Research Associate in Global Environment Modelling of Soil Organic and Inorganic Carbon
. The project is aimed to improve our in-house developed process-based computer model and use it to represent the soil ecohydrological and biogeochemical interactions across various carbon and nitrogen soil pools