362 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" positions at Monash University
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Advisory System, or data from other implantable or wearable devices. This involves consideration of both feature-based machine learning or data science approaches and neural mass parameter estimation
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. Required knowledge Strong background in machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch
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The proposed PhD project aims to build a machine learning/deep learning-based decision support system that provides recommendations on precision medicine for paediatric brain cancer patients based
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Levin Kuhlmann Research area Machine Learning We are seeking a highly motivated and innovative PhD student interested in exploring the opportunities for using AI to enhance personalisation of services and
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their performance evaluated in terms of classification accuracy, computational speed, and overall usability. Required knowledge Deep learning (CNNs, Transformers) and computer vision Knowledge distillation for model
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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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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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and multimodal applications. Required knowledge Candidates are expected to have a solid background in machine learning and Natural Language Processing. Research experience in multimodal research is
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systems. The fast growth, practical achievements and the overall success of modern approaches to AI guarantees that machine learning AI approaches will prevail as a generic computing paradigm, and will find