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Field
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examine hypotheses in a truly physiological context. In this project, you will contribute to both engineering and neurobiology. First, you will help build a light-sheet microscope optimized for voltage
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developing intelligent algorithms that can support repair and remanufacturing decisions for sustainable manufacturing? As a PhD researcher, you will create innovative machine learning solutions to optimize
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performance and durability at appropriate temperatures. Characterize materials using different techniques, and link properties to performance. Optimize reaction conditions and explore scale-up potential
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; Modeling (using Gromacs and LAMMPS software packages and MARTINI-like upscaling) the filler-matrix interfaces to optimize thermal transport, e.g., by functionalization of the nanofiller surface; Simulate and
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flow batteries (RFB), enabling affordable and durable long-duration energy storage. The approach is to use hierarchical structures, i.e. complex material layers that can be optimized to specific battery
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support each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In the FlexMobility project we propose a holistic
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antibodies) from individual serum samples. You will further develop and optimize these methods, with as special aim to enrich for antigen-specific antibodies. A secondary key objective is to improve
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increasing the attractiveness to the users, we need innovative designs where fixed and flexible services support each other. This necessitates a multidisciplinary approach bringing together optimization
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poor precision. Consequently, quantitative MRI is not used routinely in cancer care. If we can improve the image quality of quantitative MRI, it can be used in clinical routine to select the optimal
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industrial contexts; investigate optimal levels within the product structure for deploying AM in repair and spare parts support; integrate forward and reverse flows in manufacturing and maintenance