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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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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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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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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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LLMs Using AI and machine learning to improve polygenic risk prediction of disease Authorised by: Marketing, Faculty of IT , Monash University . Maintained by: Marketing, Faculty of IT . Copyright © 2024
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problem-solving skills with ability to learn and recommend solutions A strong commitment to excellence in customer service and a hands-on approach to service provision Ability to work as an effective member
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Testing AI/LLM systems Automated software testing and debugging with/without LLMs Using AI and machine learning to improve polygenic risk prediction of disease Authorised by: Marketing, Faculty of IT
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find some of our publications here: https://i.giwebb.com/research/computational-biology/ Required knowledge A solid grounding in artificial intelligence and machine learning. Learn more about minimum
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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