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the brain. This wouldn't be a typical machine learning PhD, as many aspects can only be examined on a philosophical and theoretical level. There may be scope to implement aspects in the ideas you develop
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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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Many machine learning (ML) approaches have been applied to biomedical data but without substantial applications due to the poor interpretability of models. Although ML approaches have shown
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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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algorithms for inverse molecular design. High-impact publications in premier machine learning conferences (e.g., ICML, NeurIPS, ICLR) and leading interdisciplinary journals at the intersection of AI 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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econometrics and machine learning, is highly desirable. You are well-organised and capable of managing multiple tasks to meet deadlines, with excellent written and verbal communication skills for producing clear
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methods dealing with model complexity - e.g., AIC, BIC, MDL, MML - can enhance deep learning. References: D. L. Dowe (2008a), "Foreword re C. S. Wallace", Computer Journal, Vol. 51, No. 5 (Sept. 2008
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market, Role of EVs in the grid, Power System Stability Analysis Using Machine Learning Techniques and more. Eligibility Requirements: Applicants must be Australian citizens or Permanent Residents