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Field
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cancer using graph neural networks. Our current efforts extend this to additional cancers and modalities, such as multiplexed immunohistochemistry (mIHC), immunoflouresence, spatial transcriptomics and
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investigating the neural and computational basis of anergia and effort hypersensitivity in depression. You will be responsible for: conducting behavioural, ambulatory smartphone-based and neuroimaging assessments
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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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-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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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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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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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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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