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Field
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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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Machine learning is being used to make important decisions affecting people's lives, such as filter loan applicants, deploy police officers, and inform bail and parole decisions, among other things
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The world is dynamic, in constant flux. However, machine learning typically learns static models from historical data. As the world changes, these models decline in performance, sometimes
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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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substantial experience in the use of AI applications. Demonstrated success in applying AI techniques (e.g. NLP, machine learning, generative AI, deep learning, classification/prediction models) in a relevant
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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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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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to simulate key optical features and explore optimal alignment of beamline components. The resulting data will be used to train a machine learning (ML) model, enabling automated and efficient beamline alignment
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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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tertiary education and healthcare settings. Advanced computer literacy, including proficiency in Microsoft Office and the ability to quickly learn and navigate university systems and placement platforms