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Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering , with demonstrated knowledge in machine learning, algorithms, and programming. Prior exposure to reinforcement learning
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Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering , with demonstrated knowledge in machine learning, algorithms, and programming. Prior exposure to reinforcement learning
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students on research exchanges to Guangzhou, China. Required knowledge Familiarity with Python, and passion for research Project funding Other Funding reference https://www.monash.edu.my/student-services
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diverse academic backgrounds, including computer science, data science, learning sciences, or educational technology. While prior experience with programming (e.g., Python), machine learning, or
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diverse academic backgrounds, including computer science, data science, learning sciences, or educational technology. While prior experience with programming (e.g., Python), machine learning, or
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. The project will advanced state of the art AI for NLP or vision or both and embed self-awareness modules within these systems. This will likely involve a combination of the standard methods in these
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. The project will advanced state of the art AI for NLP or vision or both and embed self-awareness modules within these systems. This will likely involve a combination of the standard methods in these
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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
ensure local relevance. Required knowledge Artificial Intelligence; Machine Learning; Computer Vision; Bioinformatics; Biomedical Engineering; Neuroscience; Genomics; Medical Physics; Data Science
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: Mastery of Deep Learning fundamentals, including experience with Vision Transformers (ViTs), Generative Models (e.g., Diffusion or VAEs), and Self-Supervised Learning. Information Theory: Familiarity
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: Mastery of Deep Learning fundamentals, including experience with Vision Transformers (ViTs), Generative Models (e.g., Diffusion or VAEs), and Self-Supervised Learning. Information Theory: Familiarity