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Field
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. The sub-project of the Phytophotonics department focuses on analysing hyperspectral imaging data for predicting infestations in field crops. The focal topics of the sub-project include: Realisation of a
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highly motivated candidate to develop models integrating machine learning and domain-specific knowledge to predict failure arising from hydrogen embrittlement. You will carry out materials testing
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integrating machine learning and domain-specific knowledge to predict failure arising from hydrogen embrittlement. You will carry out materials testing, computational model development, data processing, and
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to gain insights into the genetic underpinnings of disease and improve genetic risk prediction. We seek to build on previous expertise and methods devised by our teams (see below), including incorporating
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almost twice the size of New York's Central Park. This urban woodland area is just one of the many places where people can stroll, relax, jog, enjoy nature, or picnic. Hanover's "green ensemble
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highly accurate computational tools for predicting satellite features in XPS spectra of 2D framework materials. Your work will be based on the GW approximation within Green’s function theory. While the GW
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools
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: Develop innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry
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industry and academia, including experts on system services, converter control, flow propagation and lidar-based predictions. Among others, your tasks will comprise: Development of control algorithms
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and used to predict the development of damage. Based on this, a new maintenance strategy is to be developed that is based on the physical relationships and thus enables better consideration of critical