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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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to machine learning and deep neural networks, into the DG finite element solver to reduce computational costs while maintaining the accuracy. The key objective of this work will be to provide step-change
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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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-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
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architectures and principles from Bayesian neural networks and biological sequence models, including large DNA and protein language models. The project also aims to develop a prototype federated learning
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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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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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skills in one or more languages (Python, C/C++, or others) experience in mechanical testing profound knowledge of machine learning methods (e.g., neural networks, Gaussian processes, active learning
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their expertise together to establish neural organoid models recapitulating aspects of neural-microglia interactions in neurodegenerative diseases at Ghent University. About project MINDFUL: Lipid accumulation in
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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 structures, designing