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, you will develop 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
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prediction of queue dissolution by combining traffic flow theory with data from roadway and AMOD sensors, nonlinear optimization of the signal plan, cooperative control of traffic signals and AMOD vehicle
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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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: 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