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, using techniques such as: High-dimensional data mining Tensor decomposition Causal inference Statistical process modeling Machine Learning Applications include public transport, private vehicles, traffic
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enable the model to infer health-related information directly from NMR spectra of human blood. To this end, the model will be pre-trained using self-supervised learning on large-scale, partly synthetic
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well as knowledge representation and inference. In the research project DrawOn, new technologies for analyzing 2D digital drawings and reconstructing 3D building models will be developed. The goal of this project is
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, adversarial attacks, and Bayesian neural networks. Excellent analytical, technical, and problem-solving skills Excellent programming skills in Python and PyTorch including fundamental software engineering
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knowledge in Machine/Deep Learning with experience in discriminative models, domain adaptation, and variational inference. Excellent analytical, technical, and problem solving skills Excellent programming
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emphasis is placed on building information modelling, point cloud capturing and processing as well as knowledge representation and inference. In the research project AI-CHECK, new technologies for checking