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this data. Clustering travel needs. To define different mobility needs and motivations based on the travel data, we apply different clustering algorithms (e.g., traditional k-means, density-based clustering
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techniques to several hundred local, national and international users annually. Specific tasks Develop and apply different image analysis approaches to, for example, segment, track, and characterize cellular
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to test the effectiveness of different courses of action. In addition, uses a variety of data mining and analysis methods, using a range of data tools, building and implementing models, using and creating
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for achieving the desired mechanical and functional properties of the final structures. Unlike conventional DIW systems or the recently developed FL3DP technique that operate on fixed Cartesian coordinate with a
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networks have embraced new frequency bands in order to meet increasing user throughput demands. In the cellular world, frequency ranges (FRx) have been defined, corresponding to different carrier frequencies
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normalization and integration of data from different sources, defining appropriate strategies to deal with all ethical and privacy/security requirements; Contribute to the development, validation and integration
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experimental testing. You’ll design and run experiments, write and train algorithms, and contribute to open-source tools that may one day become industry standards. This project offers the freedom to explore
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to statistically and systematically assess the quality of official network maps. Could this be done differently? Is it possible to systematically analyse all trench documentation using data-driven methods to better
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academic year. Tenure-track faculty normally teach 2 different courses, with each section typically consisting of 22-28 students. Faculty is required to conduct research, provide high quality teaching and
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scale time series sharing. Generative models are promising solutions. Given an input set of time series, they generate a set of synthetic time series that is different from, but statistically close to