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, speaker diarization, speech separation and enhancement Research advanced neural network architectures for speech and audio Assist in supervising graduate and undergraduate students Research evaluation
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, speaker diarization, speech separation and enhancement Research advanced neural network architectures for speech and audio Assist in supervising graduate and undergraduate students Research evaluation
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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view
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simulations of PDEs, deep learning, neural networks. Our research interest: Our focus is on theoretical and computational biological physics, ranging from the study of molecules to cells. We strive to leverage
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& Perception Laboratory (NA&P Lab), led by Dr. Sabine Kastner at the Princeton Neuroscience Institute. The lab studies neural mechanisms of cognition in the primate brain. Intracranial recordings from human
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researcher to investigate the neural mechanisms underlying decision-making, using the fruit fly Drosophila melanogaster as a model system. Funded by the BBSRC, this project will combine innovative behavioural
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
metabolomics data from clinical studies. Apply deep learning models (e.g., autoencoders, variational autoencoders, graph neural networks) for biomarker discovery, disease classification, and patient
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interpretable deep neural networks is required. Candidate must have published in top journal and conference at least one scientific paper in interpretable machine learning (not explanations of black boxes) among
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geometric understanding of training in deep neural networks. The position offers excellent training opportunities at the intersection of machine learning and applied mathematics. Qualifications: - Applicants
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of biological brains. Spiking neural networks (SNNs) can offer increased processing speed and reduced power consumption, especially when implemented on dedicated hardware (neuromorphic chips or FPGAs). Standard