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to modern genomic datasets that may involve hundreds of populations. He/she will also develop probabilistic GO models inspired from the Redundancy Analysis approach and extend it by introducing Neural
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The project will include two secondments (6 months each): Stuttgart University (USTUTT): formulation of n- and p-type hydrogels Linkopings Universitet (LiU): development of the neurohybrid network The work will
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-type hydrogels Linkopings Universitet (LiU): development of the neurohybrid network The work will be done in close collaboration with the other members of the FADOS network. Further information
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(HORIZON) Doctoral Networks, ‘Bridging Communication Gaps in Human and Human-AI Interactions: The Role of Accented Speech on Neurocognitive mechanisms and Social Dynamics’ (HUM.AI.N-ACCENT) Location
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experiment. This PhD position is embedded in the EU Horizon Europe Marie Sklodowska-Curie Doctoral Network (MSCA DN) SMARTTEST project. This position is linked to Doctoral Candidate 8 – DC08. For more
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similar languages) Experience with large-scale neural network simulations Experience with analysing large-scale neural recordings Familiarity with neuroanatomy and neurophysiology Knowledge of dynamical
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multimodal systems. The emphasis is on agentic approaches, where an LLM interacts with visual tools, which may themselves be neural networks. Central challenges include enabling LLMs to reason about visual
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neuroscience and data analysis Proficiency in programming (e.g., Python, MATLAB, and similar languages) Experience with large-scale neural network simulations Experience with analysing large-scale neural
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role of neural rhuthms for inter-area brain network communicartion PHD2: The neural code for multi-item representation in working memory PHD 3: The dynamic interplay between brain and bodily rhythms in
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 3 days ago
, 3], significant challenges related to running complex AI algorithms such as Deep Neural Network (DNN) inference on lightweight platforms with limited computational power and relying on potentially