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(Shannon entropy, compressibility, effective complexity and logical depth). The data basis for the analyses are two-fold: first, recent connectome data, i. e. large datasets of all synaptic connections in
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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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that includes. Job description: We are seeking an information theorist with an academic background in mathematics or bioinformatics and a specialization in information theory (Shannon entropy, compressibility
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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