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Master's and PhD students. Candidates will be responsible for creating a collaborative work environment within and outside QGG that integrates novel innovative research programs towards the green transition
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
work. Qualifications PhD in computer science, computational biology, engineering, or related fields. Experience developing deep-learning tools for image processing, automatic monitoring of agricultural
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work. Qualifications PhD in computer science, computational biology, engineering, or related fields. Experience developing deep-learning tools for image processing, automatic monitoring of agricultural
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QGG Aarhus University seeks two Postdoctoral researchers in Quantitative Genetics of sustainable ...
Master's and PhD students. Candidates will be responsible for creating a collaborative work environment within and outside QGG that integrates novel innovative research programs towards the green transition
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and Distributed Systems: https://www.cs.aau.dk/research/distributed-embedded-intelligent-systems/ The Department of Computer Science features a broad range of synergistic activities within research and
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Systems: https://www.cs.aau.dk/research/distributed-embedded-intelligent-systems/ The Department of Computer Science features a broad range of synergistic activities within research and education in
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an LLM-assisted software framework for first-principles simulations, thereby gaining extensive experience within scientific software development and AI-driven workflow automation. The work will initially
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possess: A solid background in formal methods and process mining Experience in developing theoretical modeling and analysis frameworks Strong programming and software development skills (at least at the
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limited to one year with the possibility of extension. The research assistant position may potentially lead to the opportunity to begin a PhD position based on the candidate’s career stage and wishes. We
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collaboration with local water utilities and software developers Integrate digital urban water twins with data, applying methodologies for data assimilation, parameter estimation, and quantification of model