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of collaborative watermarking to diffusion models and audio language models based on neural audio codecs. Studying whether watermark information can be embedded in long-term content, such as speech semantics
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networks of nodes and parameters. This practice-based PhD investigates how AI can assist at the level of tool production by drafting procedural networks that the experienced technical artist can inspect
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research interests. We are particularly interested in fundamental ideas that can ultimately lead to practical algorithms rather than simply applying existing neural-network architectures. Who we are looking
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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 European doctoral network NeuroNanotech (https://neuronanotech.eu/ ), although he/she will not be a doctoral candidate within it. The doctoral work will include nanofabrication processes in clean-room
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Carnegie Mellon University Neuroscience Institute | Pittsburgh, Pennsylvania | United States | 3 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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. To this end, domain decomposition techniques, uncertainty quantification, and reduced models—possibly based on neural network training—will be considered, along with the development of theoretical results
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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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and development in basic and applied research in artificial intelligence and machine learning. Theoretical areas of interest include, but are not limited to, optimization, neural networks, and
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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