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Field
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formats available in conventional hardware are often too accurate for the needs of machine learning: they do not improve the quality of the trained model but may deteriorate it by causing overfitting
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of transmission electron microscopes and/or scanning electron microscopes Knowledge of artificial intelligence and machine learning and their applications in electron microscopy Knowledge of computer programming
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, adaptive control strategies, and hybrid energy storage solutions to address key challenges in self-powered systems under dynamic environmental conditions by: Develop machine learning or heuristic-based
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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analysis of large data sets, statistical modeling, and knowledge of at least one programming language (e. g.: R, Python and/or Julia) are required. Experience in machine learning and image recognition
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. Applying machine learning to New Zealand’s landslide inventories to model landslide location, character and dynamics. Integrating time-series and inventory data to develop new models to predict location
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Applicants should hold a relevant MSc degree in electronics, electrical engineering, computer engineering, or related fields. Required Qualification: Solid background in digital CMOS design and deep learning
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Course location Hamburg Description/content The Cluster of Excellence "CUI: Advanced Imaging of Matter", which is funded by the German federal and state governments, combines projects in physics
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the research project This project is set to explore so-called shared control between the driver of a car and the car's safety systems. By mechanically disconnecting the driver's steering wheel from
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, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical