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. ================================================ The description of the research topic "Mathematical foundations of approximation by (deep) neural networks" is as follows. Recently, the extensive use of (deep) artificial neural networks reached most of the areas
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, neuromorphic electronics based on spiking neural networks (SNNs) and the compute-in-memory (CIM) paradigm is emerging as the most promising path. While the research community has so far favored resistive Compute
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, and EHR data. Experience with modern deep learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, Pandas, NumPy, and SciPy. Familiarity with convolutional neural networks (CNNs), graph
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by connectome-constrained artificial neural networks. Candidates from outside the field of neuroscience are encouraged to apply, but must be curious, persistent, and passionate to delve
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neuroscientists pursuing advanced research and development in neuromorphic computing, artificial intelligence, and spiking neural networks across a range of applications. A strong background in theory (e.g
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opportunities at CIBR's official website: https://en.cibr.ac.cn/home At CIBR, we pride ourselves on our interdisciplinary approach, collaborating closely with top hospitals to address critical questions in brain
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representations, and flow matching) for uncertainty-aware 3D reconstruction of coronary anatomy from 2D X-ray angiography; develop physics-informed neural networks and graph-based neural operators for fast
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radars provide increasingly rich 3D information, opening the door to more advanced scene understanding and perception tasks such as 3D object detection and free space estimation. However, current neural
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determines observables of the replication program such as the Mean Replication Timing (MRT) and the Replication Fork Directionality (RFD) profiles. We proposed a strategy to train a neural network to infer
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neural network have been demonstrated using a single device. The main mission of this postdoc project, financially supported by IMITECH project, is to develop reservoir computing function based on a