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GPR and EMI imaging methods at multiple scales to enhance our understanding of the soil–root system Designing and implementing novel inversion algorithms for GPR and EMI data Identifying links between
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and spatially complex nature of MRI signals. Each MRI examination involves multiple pulse sequences, with signal acquisition being sensitive to coil placement, sensor geometry, B0/B1 inhomogeneities
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works have shown that for embedding hierarchies, we should abandon Euclidean geometry altogether and operate in hyperbolic space [1]. Our lab has published multiple papers showing that hyperbolic deep
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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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with algorithms for wearable data University of Manchester (UK): To learn mathematical modelling of hormone rhythms. University of Bristol (UK): To learn mathematical modelling of hormone rhythm
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-resolution wearable sensor streams, and endocrine test outcomes. Intelligent Artifact Detection: Develop cutting-edge Machine Learning algorithms to automatically identify, flag, and mitigate data artifacts
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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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to seamlessly integrate complex hormonal data, high-resolution wearable sensor streams, and endocrine test outcomes. Intelligent Artifact Detection: Develop cutting-edge Machine Learning algorithms
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systems strong analytical and problem-solving skills fluency in English, both written and spoken Not required, but helpful: Experience with biomedical data/algorithms An affinity for applications
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, boosted by AI-data augmentation for extrapolating spectrum patterns from multiple sources. To design a scalable computing framework using a physics-informed neural network for distributed spectrum analysis