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project TARGET-AI will bring together expertise from multiple research groups to advance the state-of-the-art in combining the most advanced techniques from deep learning/AI with rigorous statistical
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, machine learning and biophysics. Investigators at CFIN are supported by state-of-the-art research facilities, including MRI, MEG, OPM-MEG, EEG, PET, TMS, eye-tracking and more. CFIN is part of the Danish
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through the following objectives: Develop a novel approach to investigate the fluid-solid coupling effect on the performance of the CMF; Using machine-learning (deep learning) methods to develop a
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Professor that will be capable of contributing to multiple ongoing research projects in the lab. Potential projects include, but are not limited to, oceanographic characterization of deep-water habitats, GIS
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and prosthetic devices in the real-world. This PhD project offers the opportunity to work on pioneering research that combines state of the art computational modelling (deep neural networks) and
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centres, we provide an unparalleled learning environment for its 24,000 students and 13,000 staff. At Cambridge, our mission is to contribute to society through world-class education, learning, and research
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Are you looking for a PhD position where you develop state-of-the-art machine learning methods for the life sciences (geometric deep learning, transformer-based approaches, ...) with a focus on
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key regulators of inflammation and tissue remodeling in gut and skin diseases. • Apply and refine AI/ML methods, including deep learning, neural networks, and interpretable models (e.g., SHAP, BioMapAI
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are learning more and more how it relates to human health and disease. The new generation of deep metagenomic sequencing, consists in simultaneous sequencing of multiple microbial genomes at once and has
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simulations with deep learning neural networks and swarm robots, virtual reality experiments, animal communication research, and more. In a range of projects, we show that languages can effectively be seen as