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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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AI architectures such as Transformers, large language models (LLMs), vision-language models, recurrent neural networks (RNNs), and related techniques. Mathematical maturity: A solid grasp of key
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feedback control, you will uncover fundamental connections between physical dynamics and neural network representations. We seek a highly motivated PhD candidate with an excellent master’s degree in physics
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role of neural rhuthms for inter-area brain network communicartion PHD2: The neural code for multi-item representation in working memory PHD 3: The dynamic interplay between brain and bodily rhythms in
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for brain signal acquisition Implementing an on-chip neuromorphic processor with a spike encoder and spiking neural network Developing a low-power spike-based transmitter. Setting up measurement systems and
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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
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are recruiting three PhD students with distinct research foci: PHD 1: The functional role of neural rhuthms for inter-area brain network communicartion PHD2: The neural code for multi-item representation in
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and neural networks for chemical property prediction. You will be part of the Big Chemistry consortium and will also be involved in training and teaching BSc and MSc students (10% of your working time