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researchers in comparing the effects of Continuous Wave Direct Light Therapy (CW-DLT) and 5-ALA Photodynamic Therapy (5-ALA-PDT) on neural network integrity and safety. Key Responsibilities: Assist in titration
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science, physics, or related fields Coursework in algorithms, computational complexity theory, and information theory Relevant coursework and experience in spiking neural networks, and statistics A strong
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of artificial intelligence and scientific computing including physics-informed neural networks and digital twins; uncertainty quantification, high-dimensional data analysis, and visualization; generative models
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rendering, Gaussian splatting, etc.). Knowledge of graph neural networks. Experience in deploying and fine-tuning DL models, also large language or vision-language models. Practical experience on deploying ML
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networks, graph neural networks, transformers, convolutional defiltering methods, etc.) for the integration in multi-physics simulation codes You will develop code for and run large-scale multi-physics
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on the contributions of specific cortical layers and neural activity in various brain regions. Are task-specific submanifolds or spike correlation patterns limited to local neural circuits or span across brain regions
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on constrained platforms using techniques such as model compression, quantization, and hardware-aware neural network design. Investigating mechanisms that protect the integrity and reliability of deployed AI
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cycle. The data extraction will be based on existing machine learning development made by the team, in particular the use of quantile regression neural networks. There exists no other measurements
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or spike correlation patterns limited to local neural circuits or span across brain regions? Set up a network model to reproduce the main results and provide potential neuronal mechanisms. Existing
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-functional teams, conduct code reviews, and provide guidance on software design and best practices. Develop and implement advanced AI/ML models, including deep neural networks, transformers, LLM-based systems