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Research Focus We are offering a Postdoctoral position in graph machine learning, algorithms, and graph management with particular focus on: Modeling real-world spatio-temporal energy networks Developing
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fellow will conduct research on Algorithmic Verification of Concurrent Systems within the Programming Languages, Logic, and Software Security Research Group at Aarhus University. The focus of the position
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Two postdoctoral positions (3-year) in Experimental Evolution of Methanogenic Microbiomes in Bioe...
performance and stability. You will work closely with modelling and reactor-focused colleagues to integrate evolutionary outcomes across scales. Your tasks will be: designing and running experimental evolution
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performance and stability. You will work closely with modelling and reactor-focused colleagues to integrate evolutionary outcomes across scales. Your tasks will be: designing and running experimental evolution
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will be part of a research environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental
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. The candidate will design models and algorithms for learning and decision-making under uncertainty, optimized for real-time operation on heterogeneous physical devices. Finally, the position will address how
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. Experience with phase retrieval algorithms, clean room use and e-beam lithography are beneficial. The candidate will be expected to participate at international user facilities and thus will be expected
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. Experience with phase retrieval algorithms, clean room use and e-beam lithography are beneficial. The candidate will be expected to participate at international user facilities and thus will be expected
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to ensure that the developed notations and algorithms address the companies’ needs. More about the related project can be found here: https://innovationsfonden.dk/da/news-article/ai-skal-forudsige-og-forklare
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will be part of a research environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental