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to support both traditional data and the unique requirements of quantum information systems (https://www.classique.aau.dk ). CLASSIQUE will address a suite of fresh research challenges defined by
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. More information about DESS can be found at: https://www.cs.aau.dk/research/Data-Engineering-Science-and-Systems How to apply Your application must include the following: o Application, stating reasons
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and UUV to support autonomous coordination and mission execution. This includes using relative measurements, shared environmental representations, and synchronized data exchange to enable precise and
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the movements and behavior of red deer in Vejlerne using advanced drone technology and AI-based data analysis. The work will include mapping reed bed dynamics and deer trails, comparing current patterns with
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analysis, so computer vision experience is a requirement. Experience with large language models is a plus. Furthermore, as AI:Epertise is about deploying AI in the real world, we are looking for people with
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dynamic, crowded environments. As a PhD candidate, you will develop methods that combine data-driven autonomy with formal safety guarantees and validate them in real time through simulation and experimental
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Motivated Language Model Detection”) and an NNF: Ascending Data Science Investigator project (“LM2-SEC: Linguistically Motivated Language Model Security”). Your work tasks As a PhD student, you will conduct
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leading the establishment of a centralized repository for genomic data and metadata, with a strong emphasis on existing and novel methods for the evaluation of genome quality, indexing and clustering
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, computationally efficient gas radiation models suitable for CFD implementation Perform accurate CFD simulations of green fuel combustion in a CVCC and validate the CFD by detailed experimental data to be provided
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at www.es.aau.dk. Your work tasks This PhD project is part of a bigger Novo Nordisk Foundation (NNF) New Exploratory Research and Discovery grant entitled: Information Theoretic Disentanglement of the Exceptional