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Field
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multimodal systems. The emphasis is 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
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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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around us? At Maastricht University, you will investigate how individuals differ in predictive processing by combining behavioural and neural testing with computational modelling. Together with colleagues
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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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), Deep Neural Networks. Probabilistic Machine Learning and Time-series Analysis. Industrial applications of AI (energy, process industry, automation). Software development experience in teams. Programming
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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
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workings 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
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The project will include two secondments (6 months each): Stuttgart University (USTUTT): formulation of n- and p-type hydrogels Linkopings Universitet (LiU): development of the neurohybrid network The work will
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approaches that build processes by mutation operators [1], natural language processing techniques with recurrent short-term memory (LSTM) neural networks [2], and variational autoencoders (VAE
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temporarily, as needed, when needed. The goal of this project is to advance the understanding of how working memory is implemented in the human brain. To this end, the main objective is to develop a neural