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Field
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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when the shared representations between tasks are limited or trained. This project aims to test these predictions using a behavioral, neural and real-life approach. We will focus on young adults, but
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. The research will focus on identifying and characterizing ultrasonic signatures emitted by aging electronic components, and on developing physics-informed neural networks (PINNs) to model their degradation
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of current systems. Considering this, memristors are innovative electronic components that enable the creation of hardware neural networks inspired by the brain, potentially reducing the energy consumption
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, and rigorously evaluate machine learning and deep learning models (CNNs, DNNs, transformers, graph neural networks, diffusion models, multimodal models, reinforcement learning) as well as software
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learning-powered algorithms as well as hybrid approaches, combining either reinforcement learning or deep learning (Graph Neural Networks) with human-based modelling, for fully flawless and autonomous method
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large-scale spiking neural networks. In close collaboration with our Mod4Comp partners (DFG Forschergruppe FOR 5880), you will develop models of performance and energy to guide the co-design of software
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. 2) the development of computational models to enquire about the mechanisms that enable heterogeneous representations in neural networks. These models will be informed by experimental data. Duties
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. Experience in deep learning, computer vision, or neural network development. Experience with live-cell microscopy, fluorescence microscopy, or analysis of 3D/4D image data. Experience in cell biological
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Sklodowska-Curie Doctoral Network linking 21 academic, cultural, and industrial partners to develop advanced nondestructive evaluation and data-driven digital tools for paintings and 3D artworks (https