344 algorithm-development-"Multiple"-"Prof"-"Prof"-"Simons-Foundation" "U.S" positions at Monash University
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the development of Explainable AI Systems that can provide explanations of AI agent decisions to human users. Past work on plan explanations primarily focused on explaining the correctness and validity of plans. In
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This PhD project is part of a larger project that aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain
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at prediction and pattern recognition tasks but still fails at very simple planning and decision-making problems. This project will develop predictive and prescriptive analytics algorithms that combine
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This PhD project aims to mitigate the data scarcity of new NLP and Multimodal applications by developing novel active learning algorithms. In this project, the student will leverage large foundation
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Seizure prediction algorithms will be developed using the one-of-a-kind ultra-long-term human intracranial EEG dataset obtained from the Neurovista Corporation clinical trial of their Seizure
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collaborative research, further developing expertise within the field. Key Responsibilities Conduct research on complex quantum processes, particularly using the process tensor formalism, and leverage practical
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. With the widespread adoption of ML algorithms for data analysis and decision-making, preserving the privacy of individuals' data has become a paramount concern. The project focuses on exploring
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for metabolic acidosis in critically ill ICU patients You will be responsible for managing trial operations across multiple clinical sites, maintaining study documentation, facilitating ethics submissions
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on clinical trials Well-developed planning and organisational skills, with the ability to prioritise multiple tasks and set and meet deadlines Capacity to work in a collegiate manner in a team environment and
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Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available