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Field
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, natural hazards management or related fields Interested in protective forests and their management Good quantitative skills (e.g., data analysis, simulation modelling, remote sensing) Good communication
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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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that there is no separate scholarship application process as all applications will be considered automatically. All scholarships will be granted on the basis of academic merit only. Academic admission
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Your Job: The conventional, manual co-design of algorithms and hardware is slow and inefficient. Our group develops methods and tools to automate the co-design process. The core of this project is
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Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor extraction and micromechanical simulations (MCRpy, DAMASK) Vary the material processing parameters, which
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algorithms for application parallelization, simulators and virtual platforms for application- and architecture exploration, hardware/software co-design and operating/runtime systems. Typical application
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slow and inefficient. Our group develops methods and tools to automate the co-design process. The core of this project is the development of meta-optimization techniques that can automatically search
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using functional 2D materials. • Organic synthesis and typical characterization methods, as NMR, FTIR, MS, EA, UV-vis and fluorescence spectroscopy. • Methods for 2D materials processing and
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-based processing. This project will investigate event-driven learning approaches in the context of RL in an event-triggered fashion. Data efficiency will be improved by using meta-learning and pre
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-selectivity. Calculations and simulations will guide fabrication of experimental prototypes, to be tested in beamtime experiments at world-leading neutron science facilities (e.g. ILL, FRM II). Experimental