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16 Jan 2026 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Educational sciences » Education Educational sciences » Learning studies Engineering
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and machine-learning-driven analyses create opportunities for high-frequency, minimally invasive measurements. Proof of concept will be used in sheep, cattle or pigs, initially based on data from
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and digitizing archival data, strong knowledge of causal inference methods, good command of R and Python. Knowledge of machine learning methods is an asset. Strong command of English; command of either
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storage, but their widespread deployment is limited by challenges in energy density, stability, solubility, and cost of electroactive redox compounds. The PhD candidate will develop and apply machine
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and behavioural speech features. Integrate neuroimaging, speech and clinical data using multivariate and machine-learning approaches (e.g. UMAP). Investigate the effects of deep brain stimulation
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machine learning packages (e.g.PyTorch). Completed academic courses in AI or machine learning. Interest in societal, ethical and philosophical questions. We consider it an advantage if you bring one or more
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is made for you! Information We invite highly motivated students with a strong background in mathematical control theory, and a keen interest in machine learning to apply for the PhD position within
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. Methodological Approach Candidates will develop and apply state-of-the-art machine learning techniques, including deep learning, representation learning, variational autoencoders, and graph-based models. A strong
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interest in interdisciplinary research at the intersection of AI and neuroscience (NeuroAI), and human vision; A background in machine learning, deep learning, and/or representational alignment research
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adaptation, synthetic data generation, and cross-modal learning to enable models that generalize across defect types and machine configurations. This ensures scalable, accurate defect detection even in low