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English English PhD Research Fellow in Machine Learning and Distributed Data Processing Apply for this job See advertisement Job description Position as PhD Research Fellow in Machine Learning and
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particular focus on developing fundamental AI algorithms and methods that can be used in systems for real-time creative and artistic settings. The candidate will be part of a team that creates algorithms
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-based methods to achieve personalised and novel outputs. This position will have a particular focus on developing fundamental AI algorithms and methods that can be used in systems for real-time creative
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) on physical robots. • Use evolutionary algorithms to optimize both the robot’s body and brain together. • Apply quality-diversity methods to discover a wide range of high-performing designs
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and iteratively improved. • Integrate and test autonomy stacks (perception, learning, planning) on physical robots. • Use evolutionary algorithms to optimize both the robot’s body and brain
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a compositional guide. This includes assessing the distribution of specific elements during condensation in the stellar nebula, gathering elemental availability for the major rock-forming and volatile
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of this position involve the implementation of chemical compositions for forming exoplanets, using PLATO stars as a compositional guide. This includes assessing the distribution of specific elements during
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Carbon monOxide Mapping Array Project (COMAP) line intensity mapping (LIM) experiment, aiming to map the large-scale distribution of star-forming carbon monoxide around Cosmic Noon (targeting redshifts
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for distribution modeling and analysis of experiments, and new tools may be developed. The main tasks of the postdoctoral fellow will be within field-based (WP1) and model-based (WP2) approaches. The postdoctoral
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Professor research work will include the following topics and tasks: Develop algorithms and theory for inversion of data collected by RIMFAX and other CENSSS instruments. Contribute to modelling, inversion