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This Masters or PhD project aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain ML predictions and
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Machine learning, dynamical systems theory, control theory, signal processing, network theory, neuroscience are all relevant and a student should have strong knowledge in at least one of these and a
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reinforcement learning. In International conference on machine learning (pp. 2107-2128). PMLR. - Péron, M., Becker, K., Bartlett, P., & Chades, I. (2017, February). Fast-tracking stationary MOMDPs for adaptive
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while inferring underlying physiological changes. Required knowledge Machine learning, dynamical systems theory, control theory, signal processing, time series analysis, neuroscience are all relevant and
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experience with programming (e.g., Python), machine learning, or educational data is beneficial, it is not a strict requirement. The project provides ample opportunities to develop these skills over time. What
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develop team members, and deliver outstanding customer service. You will be a collaborative and proactive professional with sound computer skills, a commitment to safety and compliance, and the ability
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artificial agents learn to cooperate? Counting wildlife by voice – uncertainty-aware population estimates from bioacoustics Multimodal AI and Machine Learning for Diabetes-Related Complications: Integrating
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Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
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agents learn to cooperate? Counting wildlife by voice – uncertainty-aware population estimates from bioacoustics Multimodal AI and Machine Learning for Diabetes-Related Complications: Integrating Clinical
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" "Machine-learning-based imaging processing" webpage For further details or alternative opportunities, please contact: [email protected].