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of the project is to design, model and simulate neural networks based on magnetic skyrmion nucleation and propagation. The second objective is to fabricate these hardware neural networks, characterize
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implement and train neural network architectures, including Physics-Informed Neural Networks (PINNs), in order to integrate physical constraints into the learning process and improve the identification and
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:this project pioneers a new paradigm of General Genome Interpretation (GenGI) models by combining DNA Large Language Models (DLLMs) with Deep Neural Networks to predict human phenotypes directly from Whole Exome
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. The work will be primarily computational, focusing on the development of deep neural network model architectures and their training. It will involve extending the preliminary results we have already obtained
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do they also influence the early recognition of these symptoms by family members and the healthcare team? What links might these factors have with clinical, cognitive, or neural data? Finally, how do
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FieldComputer scienceYears of Research Experience1 - 4 Research FieldMathematicsYears of Research Experience1 - 4 Additional Information Eligibility criteria Skills/knowledge: computer vision, neural networks
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Information Eligibility criteria Applicants should hold a PhD in theoretical chemistry, physics, materials science, or a related field; -demonstrate strong expertise in machine learning (regression, neural
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validated at CPPM. In parallel, the candidate will improve data reconstruction algorithms by using artificial intelligence techniques (e.g. neural networks), to optimize the separation between signal and