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behaving mice, and advanced modeling + machine learning analyses. Please read more about our research at www.apostolideslab.org . Key questions we want to answer are: How do neural circuits extract
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4 PhD Fellows in Deep Learning at Visual Intelligence Research Centre and UiT Machine Learning Group
Stig Brøndbo 17th June 2025 Languages English English English Faculty of Science and Technology 4 PhD Fellows in Deep Learning at Visual Intelligence Research Centre and UiT Machine Learning Group
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the direction of A/Prof Claudia Szabo in the School of Computer and Mathematical Sciences at the University of Adelaide. The project is a collaboration with Defence Science and Technology Group, within the Combat
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synaptic and cellular studies using patch-clamp electrophysiology, large-scale population recordings using 2-photon Ca2+ imaging in awake behaving mice, and advanced modeling + machine learning analyses
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developing machine learning or data science approaches for patient stratification and genetic association analyses using cardiac magnetic resonance imaging in biobank populations. Successful applicants will
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the university, genomic and metabolomic measures, offering novel potential to explore the physiological basis for imaging measures and apply machine learning in a radiological context. You will join an established
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backgrounds: Molecular biology, protein engineering, biochemistry. Optical engineering, fluorescence microscopy, image analysis: Development of microscopes and data analysis pipelines used to acquire and
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skills in image processing or genomic data analysis. Proficiency in rodent neurosurgeries. Familiar with Linux OS, virtual environment working and batch scripting. Programming proficiency in R, Python
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problems in the health sciences, including fields such as healthcare informatics, movement and rehabilitation sciences, medical imaging, remote sensing, computer vision, mental health, data fusion
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Australian National University | Canberra, Australian Capital Territory | Australia | about 2 months ago
approaches to model uncertainty for learned computer vision systems, including dense prediction. The position will develop novel methods for deep learning in computer vision that accurately quantify their own