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Field
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at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need
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energy use more efficient. We develop new optimization methods, machine learning algorithms, and prototypical systems controlling complex energy systems like electric grids and thermal systems for a
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. This could include scholarship in topics such as, theoretical or applied data analytics; algorithm development and solving concrete problems for science, industry, and society within various application
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and distributed control intelligence that can be applied to solve these problems through the application of machine learning, intelligent optimization techniques, automated fault detections and
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(DevOps and CI/CD) Computer Science Topics: Programming Analysis of Algorithms Operating Systems and Distributed Systems Computer Organization and Architecture Artificial Intelligence Other related topics
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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13.01.2020, Wissenschaftliches Personal PhD position at the Chair of Algorithms and Complexity. Candidate shall work on approximation algorithms for scheduling problems in parallel and distributed
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The existing deep learning based time series classification (TSC) algorithms have some success in multivariate time series, their accuracy is not high when we apply them on brain EEG time series (65
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challenging data problem. Weak signals from collisions of compact objects can be dug out of noisy time series because we understand what the signal should look like, and can therefore use simple algorithms