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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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inductive biases, we aim to identify key mechanisms that drive rapid learning in the visual system. The goal is to create a robust mechanistic neural network model of the visual system that not only mimics
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theoretical research is focused on embodied neuroAI, recognising that the body influences biological neural networks, the continuity of actions, and sensory inputs. Leveraging advancements in Drosophila genetic
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Tubes via Recurrent Neural Networks for Planning Robust Robot Motions". In ECAI 2024 (pp. 4385-4392). IOS Press. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UPR8001-MARCOG-003
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knowledge of wireless communications, and signal processing. You have at least intermediary knowledge of machine learning algorithms, including federated learning, split learning, and graph neural networks
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and neural networks for chemical property prediction. You will be part of the Big Chemistry consortium and will also be involved in training and teaching BSc and MSc students (10% of your working time
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection. Comparison with known analytic methods and
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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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directions will be pursued to enhance column generation using machine learning. The first line of research focuses on improving scalability by using Graph Neural Networks to identify and eliminate non