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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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Time series are an ever growing form of data, generated by numerous types of sensors and automated processes. However, machine learning and deep learning methods for analysing time series are much
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an electrical and computer systems engineering degree in the Faculty of Engineering. Total scholarship value $20,000 Number offered One at any time See details Farrell Raharjo Clive Weeks Community Leadership
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healthcare, finance, environmental monitoring, and beyond. While recent advancements in foundation models have shown tremendous success in NLP and computer vision, the unique characteristics of time series
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Candidates should hold a previous degree (Bachelor’s and/or Master’s) in Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering, with demonstrated knowledge in machine
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species' distributions. This project harnesses research in ecological and agent-based modelling, machine learning, and AI to increase the predictive power of models of species’ distribution shifts via “data
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collaboratively with colleagues, strong computer literacy with proficiency in Microsoft Office and relevant university systems, and the ability to learn new software as required. Experience working with schools
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MML for well-behaved models, and has been successfully applied to diverse problems including hypothesis testing, clustering, and machine learning. Aim 1: Theoretical Investigation of MML Properties
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this project, we will develop automated approach to detect the defects in AI systems, including LLMs, auto-driving systems, etc. Required knowledge - self-motivated, willing to spend time and efforts in research
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. Excellent interpersonal and communication abilities. High-level computer literacy and database management. Discretion, tact, and a commitment to confidentiality. This is your opportunity to play a key role in