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Field
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multi-electrode arrays to evaluate the activity of neural network formation Testing the inter-laboratory reproducibility of the model between the BfR, Berlin, and the TiHo, Hannover Preparation
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operational employment. This doctoral research will thus leverage the power of graph neural networks – a novel ML architecture, capable of learning fundamental physical behaviour by modelling systems as graphs
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algorithms. Graph Neural Networks. The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another field
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. 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 annotations). The research will be conducted
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degree in computational engineering, mechanical engineering, computer science, applied mathematics, physics or a similar area very good programming skills in Python good prior experience with neural
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research areas: Generative AI for Medical Imaging and Digital Biopsies Develop and interpret deep neural networks (DNNs) for automating non-destructive tissue-based analyses using high-parameter medical
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., neural networks, Gaussian processes, active learning) interest in materials science (e.g., SCC) excellent knowledge of English (written and spoken) high degree of motivation, creativity, and flexibility
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the visual system. The goal is to create a robust mechanistic neural network model of the visual system that not only mimics its processing capabilities but also its adaptability, leveraging early
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the Spiking Neural Network (SNN) itself. However, close collaboration with another PhD student working on the SNN hardware design is expected to ensure seamless signal interfacing and system integration. Key
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-informed data analytics tools for the predictive maintenance (PdM) strategy applications to high-value critical assets. Among others, the recently developed Physics-informed Neural Network (PINN) technique