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frameworks to maximise compression ratios. We will use predictor models which estimate projections or slices, storing only differences between the prediction and original data. Because errors are small and
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in underground facilities. The project aims to evaluate sensor technologies, design and optimize multi-sensor monitoring networks, and develop advanced detection and localization algorithms adapted
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interface of machine learning, statistics, probability, and with applications in statistical genetics, developing new theory, algorithms, and scalable implementations. Starting date as soon as possible and
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, international students and EU students without Settled Status will need to cover the difference between the home rate and the international. Visas and associated costs are not covered. Closing date: 20th April
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imaging, based on absorption, provides good image contrast between high- and low-density materials, such as bones and soft tissue. However, it cannot distinguish subtle density differences between soft
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applications in statistical genetics, developing new theory, algorithms, and scalable implementations. Starting date as soon as possible and upon individual agreement. The fellowship period is three years. A
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a focus. Traditionally, this is done through iterative algorithms (‘trial and error’). In this project, we aim to develop a radically different approach where the correct shape is computed using a 3-D
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that captures relevant features at different length scales and integrates them into a single reconstruction volume. This PhD project focuses on learning-based phase retrieval in the weak holographic regime
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. Therefore, we need an imaging scheme that captures relevant features at different length scales and integrates them into a single reconstruction volume. This PhD project focuses on learning-based phase
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The Fraunhofer Institute for Algorithms and Scientific Computing SCAI in Sankt Augustin, near Bonn, has around 180 employees who research and develop innovative methods in the field of computational