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to fuzzy-based algorithms. You will base your work on two different flood classification approaches, namely a hydrology-based one and a hydrograph-based one and compare these regarding their ability
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to source localization based on microphone arrays or distributed sensors. This PhD project will focus on the development of novel methods and algorithms for airborne noise source localization in generic urban
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responsibilities include: Development of a flood classification framework for flood type prediction Comparison of different ML algorithms in a sensitivity study Communication with stakeholders Development of open
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behavioral component as it would be interesting to study how consumers perceive algorithmic discrimination of different kinds and how companies can mitigate negative perceptions. During their PhD
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to the research; Good understanding of computer architecture; Basic understanding of MRI algorithms is a plus; Understanding of AI and its practical implementations; The ability to work in a team and take
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to track how the prevalences of different strains in a mixed sample change over time. Your role: You will develop and implement algorithms to find, quantify and track mutations in evolving populations
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. For example, we would like to be able to track how the prevalences of different strains in a mixed sample change over time. Your role: You will develop and implement algorithms to find, quantify and track
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limitations that cannot be overcome with bigger models and larger datasets. A critical issue is the embedding of hierarchies, for which a different geometry is better suited, namely hyperbolic geometry. Seminal
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, enabling energy-efficient, quiet, and long-duration monitoring of ecosystems. The research will integrate novel lightweight perception modalities for robust perching in the wild, agile control algorithms
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models for the integration of healthcare data specifically incorporating patient preferences. This includes challenges such as how to adapt to different perspectives and data models as well as ensuring