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to be developed. One promising research direction is the use of physics-informed deep learning, such as physics-informed neural networks or deep neural operator networks. Tasks: Work in a team on national
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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
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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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interaction networks that contribute to the pathogenesis of these diseases. This is a full-time, non-tenure-track position working in the Laboratory of Molecular Therapeutics. The appointment is annually
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an exciting PhD scholarship to tackle one of the most pressing environmental challenges—detecting methane emissions from space using advanced neural network technology. This unique opportunity is part of
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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network attractors, funded by The Leverhulme Trust. This is a brain inspired project in the field of Neurodynamics. Networks of oscillators are ideal candidates for modelling patterns of functional
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advantage if you can provide a link to a code repository of a project where you are the main contributor.- Experience in implementing machine learning methodologies, specifically neural network-based methods
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and prosthetic devices in the real-world. This PhD project offers the opportunity to work on pioneering research that combines state of the art computational modelling (deep neural networks) and
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explainability of Artificial Neural Networks (ANNs). In collaboration with our medical project partners, we hope to leverage the results of this ANN-based study to better understand social cognition in humans, in