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Eberhard Karls University Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 14 hours ago
resonance imaging (fMRI) to measure functional activation during an emotion regulation paradigm. Besides, females will also undergo a resting-state measurement, diffusion tensor imaging and an anatomical scan
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. This entails: • You conduct research at the interface between Quantum Information and Quantum Many-Body Physics, where the focus of your research will lie on the study of topological order using tensor network
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of your research will lie on the study of topological order using tensor network methods. You continuously stay informed about the state of the art in your field. You present your research plan
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. This entails: • You conduct research at the interface between Quantum Information and Quantum Many-Body Physics, where the focus of your research will lie on the study of topological order using tensor network
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expertise will extend to various areas, including quantum Monte Carlo, machine learning, quantum computing, quantum machine learning, and tensor networks. These and other techniques will allow us to confront
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communications. The team specializes in multi-sensor methods, tensor decompositions and component analysis for the joint processing of multimodal data, notably in the context of invasive (intracardiac electrograms
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, Diffusion Tensor Imaging (DTI), Ultrasound, muscle stimulation, electromyography (EMG), and motion capture. Conducting human anatomical specimen dissection studies to obtain in-vitro data for model
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train these methods in a closed-loop