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on theoretical and computational aspects of quantum many-body systems, including Tensor Networks, Neural Quantum States, Stabilizer formalism, Complexity measures such as entanglement and quantum magic, quantum
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and is associated with the management of the Institute's own 500 m² clean room facilities (https://www.igm.uni-stuttgart.de/en/research/clean_room_lab/ ). We are looking for an individual with
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Lab Technician II will provide research-related supervision and oversight of the activities in the lab. They will also work on existing and new studies investigating behavioral and neural correlates
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 3 months ago
fitted with sensors, we can also access precise physical measurements. Recent work in AI-for-Science has shown that neural-network-based meta-models can also assimilate measurements. Machine learning
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. ... (Video unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open video) About the position The Neural Dynamics and Computation group (www.gonzalocognolab.com), led
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networks) making-up the nervous system, brain/cranial structures and basic functions (physiology) and complex neural systems related to sensation/perception, motor movement, attention and memory, stress
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environmental perturbations become biologically embedded through the microbiome, we will define the signalling networks and neural circuits that shape brain and behavioural function. This programme seeks
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Duke University - Biochemistry & Cell Biology | Durham, North Carolina | United States | 2 months ago
positions in deep neural networks for biology. Successful candidates will have a strong interest in developing, interpreting, and applying new AI algorithms and models, motivated by biological research in
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apply machine learning and deep learning models (e.g., graph neural networks, generative models, transfer learning) for materials property prediction, interpretation, and inverse design. Perform high
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Qualifications Experience with graph neural networks, machine-learning interatomic potentials, or related scientific machine-learning methods for atomistic systems. Familiarity with uncertainty quantification