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Lösungen für inverse Probleme in der diagnostischen Biomechanik beizutragen, mit besonderem Schwerpunkt auf Elastographie. Das Projekt baut auf unserem kürzlich entwickelten Weak Neural Variational Inference
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Lösungen für inverse Probleme in der diagnostischen Biomechanik beizutragen, mit besonderem Schwerpunkt auf Elastographie. Das Projekt baut auf unserem kürzlich entwickelten Weak Neural Variational Inference
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training and inference algorithms for specific edge hardware platforms. Implement and test models on neuromorphic hardware. Contribute to research proposals and funding applications. Publish and present
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cars. • Optimise AI training and inference algorithms for specific edge hardware platforms. • Implement and test models on neuromorphic hardware. • Contribute to research proposals and funding
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coherence, optical), including cross-modal fusion and modality distillation • Design a causation analysis framework combining deep learning with causal discovery & inference to quantify the influence
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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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required to create a holistic picture. Such additional information can improve the performance, help to reveal biases, or may enable to perform causal inference. We are interested in developing statistical
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the possibility of an extension. TASKS: Mathematical modeling and development of inverse methods (e.g. Bayesian inversion, optimization based methods, sparsity promoting methods based on L1-norm minimization and
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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