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sequence programming (e.g., IDEA/ICE) and contemporary image reconstruction techniques (e.g., compressed sensing, parallel imaging, model-based or deep learning reconstructions). Knowledge of radial data
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sequence programming (e.g., IDEA/ICE) and contemporary image reconstruction techniques (e.g., compressed sensing, parallel imaging, model-based or deep learning reconstructions). Knowledge of radial data
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, biologists, and data scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and
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and training your own AI-based models for image segmentation or image compression, as demonstrated by Git repositories Experience in supervising students and young scientists Good knowledge of materials
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scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and machine learning
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, biologists, and data scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and
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in the field of heat pumps and refrigeration systems Knowledge of English. Specific Requirements Modelling and Simulation of CO2 Refrigeration Systems based on liquid piston compression, validated with
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environment in the School of Physical Sciences. Maintain cell cultures and perform imaging studies and analysis on these cultures. About You Essential: A PhD in physics, biology, applied mathematics, computing
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, encryption/decryption and compression; use of microelectronics devices (including COTS); implementation, inference, verification and validation of algorithms** on processing hardware platforms for space