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. Investigate and implement optimum sampling strategies, including sparse and compressed sampling techniques. Explore the applicability of neural networks in clinical workflows, ensuring solutions are practical
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management techniques, IoT-based vehicular monitoring systems, and SDV architectures. Explore recent advancements in neural networks, reinforcement learning, and predictive modeling techniques for power
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organizational levels of the brain – from molecular and cellular processes to complex neuronal networks and behavior. The research group “Behavioral Neuroscience” focuses on decoding the exact mechanisms behind
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, such as physics-informed neural networks (PINNs), and apply them to regenerative processes. Collaborative by nature – You enjoy working across disciplines and feel at ease in an international
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analogous neural circuitry and shared molecular pathways have established songbirds as the model system of choice for human speech learning and fine motor control in general. The PhD candidate will use
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of the Biomaterials and Tissues of the Future. https://cordis.europa.eu/project/id/101226431 This network has 8 host institutions hiring doctoral candidates: Uppsala University, Universitat Politecnica de Catalunya
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methods for data assimilation; and graph-based multi-scale neural network models. While the developed methods will be broadly applicable, particular emphasis will be put on the problem of inferring gas
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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Bayesian neural networks. Excellent analytical, technical, and problem-solving skills Excellent programming skills in Python and PyTorch including fundamental software engineering principles and machine