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Postdoctoral Research Associate in Global Environment Modelling of Soil Organic and Inorganic Carbon
or a related discipline, with an emphasis in numerical computation intermediate to advanced skills in large data analysis and manipulation, including programming in different languages including Matlab
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laboratory techniques (mammalian and bacterial) Multi-omics data analysis CLSM and SEM imaging skills Strong analytical, problem-solving skills and Excellent written and verbal communication skills and ability
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establishments (Université Côte d'Azur, CNRS, INRAE, INSERM ...), but also with the regiona economic players. With a presence in the fields of computational neuroscience and biology, data science and modeling
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at the Humanitas University campus. Their common mission is advancing science to make a difference in patients' life. Humanitas Research is guided by unmet clinical needs and it leverages high-end technologies
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: Statistical signal/image processing, deep learning, machine learning, neuromorphic computing Good communication skills and an appropriate publication record are essential. Solid knowledge of Python and C++ is
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uncover how epithelial cells organise in space and time under different physico-chemical environments to drive self-organisation processes, like condensates, that shape mesoscale structures enabling tissue
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on identifying the brain regions associated with different cognitive processes, but more recent studies seek to understand the nature of the information stored in various brain regions, or representations, and how
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Engineering, Science and Theory (NESTiD) Scientific Computing (SciComp) Vision, Imaging and Visualisation (VIViD) We are ranked 4th in the UK in the Complete University Guide 2024. For more information, please visit our
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Vision Group at the division of Signal processing and Biomedical Engineering develops intelligent systems for automatic image interpretation and perceptual scene understanding. Our research spans both
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potential applications in audio and music processing. Standard neural network training practices largely follow an open-loop paradigm, where the evolving state of the model typically does not influence