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efficient, adaptive use of multistable networks, offering an alternative to energy-intensive digital systems with rigid bits and separated memory and computation. In this project, we aim to design and realize
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transferable skills such as HPC software development, extensive physical understanding of turbulent flows, and experience in modern ML architectures such as neural operators and transformers. The expected start
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such as neural operators and transformers. The expected start date is February 2026, and the PhD duration contract is 4 years. Please contact Dr. Bernat Font (b.font@tudelft.nl ) for more information. Job
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addresses this challenge in two ways: We investigate the fundamental neural mechanisms that control movement. We explore engineering-based solutions to restore function when these pathways are disrupted. As a
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shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational
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-tracking and behavioural measurements. As a PhD Candidate, you will be part of an international network of 13 research labs located throughout Europe working on the EU-funded HUM.AI.N-ACCENT project
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shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational
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inductive biases, we aim to identify key mechanisms that drive rapid learning in the visual system. The goal is to create a robust mechanistic neural network model of the visual system that not only mimics
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transparent and intelligible. Although explainable AI methods can shed some light on the inner workings of black-box machine learning models such as deep neural networks, they have severe drawbacks and
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of black-box machine learning models such as deep neural networks, they have severe drawbacks and limitations. The field of interpretable machine learning aims to fill this gap by developing interpretable