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engineering, computational neuroscience, artificial neural networks and bio-inspired robotics: "Rhythmic-reactive regulation for robotic locomotion" (Supervisor: Prof Fulvio Forni) will apply techniques from
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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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applied to control problems or tiny RL scenarios. Explore digital hardware realizations of the proposed RL algorithms within existing spiking neural network chip designs. Quantitative comparisons with
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internships at KU Leuven (Prof. Rodolphe Sepulchre) and at TU Delft (Prof. Charlotte Frenkel) Participate in yearly retreats organized by the doctoral network participants Support the dissemination of software
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(HORIZON) Doctoral Networks, ‘Bridging Communication Gaps in Human and Human-AI Interactions: The Role of Accented Speech on Neurocognitive mechanisms and Social Dynamics’ (HUM.AI.N-ACCENT) Location
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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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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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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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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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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