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Field
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learning algorithms, and prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity distribution grids and thermal energy systems for a sustainable future
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the project to have well-distributed data both in space and time. This will ultimately lead to higher quality (more spatially and temporally accurate, complete, precise) 3D models. However due to the complexity
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support to faculty and administrative staff. Continuously monitors OIRDS applications and program scripts to distribute workloads efficiently across servers, ensuring optimal system performance. Implements
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algorithms, and prototypical systems controlling complex energy systems like buildings, electricity distribution grids and thermal systems for a sustainable future. These systems coordinate 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
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manipulation detection. Another focus is the development of algorithms for the areas of virtual product development, intelligent actuator-sensor systems and audio for the automotive sector. There are currently
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(for example insect-eye–inspired motion detectors for planetary landing and insect-inspired navigation algorithms) to evolutionary and neuromorphic approaches to autonomous control, as well as soft-robotic
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, organization, compression, analysis, and visualization of georeferenced or geometric data in large scales. We put emphasis on methods of distributed computing, machine learning, image and text analysis
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and often different from the canonical types of data used to benchmark machine learning (ML) algorithms. In this opportunity, we will be evaluating how state-of-the-art ML techniques can be used