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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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, 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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-learning architectures for sequential data (e.g., Transformers, graph neural networks, state-space models). Experience with OpenCV, GPU-accelerated inference, Docker, and modern software engineering
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Carnegie Mellon University Neuroscience Institute | Pittsburgh, Pennsylvania | United States | 2 months ago
alignment, normative agentic models of animal behavior, mechanistic models of memory or learning in neural networks, descriptive models of invariant representations and dynamics, and embodiment in both
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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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. ================================================ 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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Academic Job Category Faculty Non Bargaining Job Title Postdoctoral Research Fellow – Human Neuroimmunology, Brain Organoids and Multi-omics Department MS Research Network | Department of Medicine
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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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Qualifications Experience with graph neural networks, machine-learning interatomic potentials, or related scientific machine-learning methods for atomistic systems. Familiarity with uncertainty quantification
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your academic career? Your interests lie in the field of machine learning techniques, particularly artificial neural networks, and deep learning? And you would like to continue your research