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Field
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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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profile, experience and research proposal. Planning and autonomy: The objective is to study the state of the art of planning algorithms that would support onboard autonomous operations of a rover system on
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images. However, the current limitations of desktop computers in terms of memory, disk storage and computational power, and the lack of image processing algorithms for advanced parallel and distributed
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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