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BIOS-06/A - A synaptic mechanogenetic technology to repair brain connectivity - CUP: J93C22002400006
of neural networks. The technology will be validated in mouse models of neurological disorders. Where to apply Website https://pica.cineca.it/units Requirements Additional Information Eligibility criteria
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scope of the PhD position(s) fall within the areas of novel low-complexity neural network architectures, generative audio techniques, and the integration of large language and speech foundation models
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) methods. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR5253-MOUBEN-002/Candidater.aspx Requirements Research FieldChemistryEducation LevelPhD or equivalent Research FieldChemistryEducation
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have demonstrated expertise in Natural Language Processing (NLP) and teaching. They should have the ability to teach both classical statistical methods and modern “black-box” approaches, including neural
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research projects will be considered.) Technical expertise in machine learning and model fine-tuning – 10% Demonstrated experience with neural network training, loss function design, embedding-based models
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dynamic differential calorimeter (DSC) using a neural network to be developed for predicting a pseudo-DSC signal from microscope images Transfer of the evaluation routine to a multi-sample test rig
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. A particular focus of the project will be on: 1) Graph Neural Networks for cosmology, neutrino and/or collider physics, 2) Domain adaptation methods / model robustness, 3) Uncertainty quantification
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Electroencephalography (EEG) and magnetic resonance imaging (MRI) can detect early alterations in neural networks that manifest as cognitive and sleep-wake cycle disorders in the early stages of Alzheimer's disease
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month ago
, promoting trustworthiness. Four research profiles are available under this context: Profile 1 (P1) – Development of methodologies based on graph neural networks and foundation models to integrate multiple
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Starrydata2). The work will include the implementation of machine learning models (neural networks, random forests, SISSO), generative approaches for predicting crystal structures, the use of machine learning