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of performance, as they are very cautious by design. This, in turn, makes them less practical for problems where speed is of utmost priority. On the other hand, offline learning, such as Deep Learning, often
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linguistics / sociolinguistics / cultural evolution. Experience in designing and conducting iterated learning, dyadic communication, or artificial language learning experiments. Experience in computational
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this project, you will work with the team at M4i, Maastricht University to design, develop, and test the novel IR-MALDI-MSI source. Then, in the next phase, you will install and operate this source at HFML-FELIX
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design material and explore an ‘aesthetics of uncertainty’ in the design of smart home systems. Instability is an attribute of dynamical models and systems. With increasing connectivity between different
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this specific structured data. How can we perform inference tasks to learn hidden patterns, like community structure or hidden hierarchies? How can we incorporate domain knowledge to design interpretable models
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to shield them from looming menace. Yet some parents respond differently. These parents not just refrain from protectiveness but actively encourage their children to explore and connect with others. Our
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such cognitive constraints affect individuals differently across key stages of language development, including phonological and semantic learning. The candidate will design and conduct experiments investigating
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different rescripting strategies reduce the impact of emotional memories, and whether these changes in memory predict subsequent symptom reduction in a student population. This work will be followed by
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into answering counterfactual questions. Using remote sensing multimodal time-series data and Earth foundation model embeddings, you will design and develop causal machine learning models tailored for dynamic
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explainable AI (XAI) methods with user-centred interaction design, combine machine learning with alternative AI methodologies (e.g., rule-based reasoning, knowledge graphs, hybrid approaches where relevant