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Field
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do they also influence the early recognition of these symptoms by family members and the healthcare team? What links might these factors have with clinical, cognitive, or neural data? Finally, how do
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processing (NLP), specific knowledge of machine learning techniques applied to the fields mentioned above (such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), transformers-type
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that methodological advances are developed with direct translational and scalability considerations. Responsabilities: Lead the development of hybrid foundation model-graph neural network architectures for gene
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computational imaging specialist – experience in quantitative image analysis, scattering modeling, signal processing, machine learning, or neural-network-based data interpretation. The project is closely
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from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems, neuroscience, and safety and security
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software. (0-35) Experience in the application of advanced machine learning techniques (e.g., graph neural networks, reinforcement learning, probabilistic models, or latent representations) to biomedical
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imaging specialist – experience in quantitative image analysis, scattering modeling, signal processing, machine learning, or neural-network-based data interpretation. The project is closely connected
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, dynamical systems, statistical machine learning, and neural time-series data. The goal is to better understand principles and mechanisms underlying distributed brain network computations through the dual
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methods, which could include but are not limited to: Kriging surrogate, Polynomial Chaos Expansion (PCE), and Physics-Informed Neural Networks (PINNs) Contribute to the strategic direction of research
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, tissue sections, RNA/DNA, tabular data) for predictive modelling using software such as Python Documented experience of neural networks, image processing, deep learning algorithms, and data visualization