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PhD Project – Supervised and knowledge-guided machine learning approaches for quantifying and identifying microorganisms in water and wastewater treatment Job No.: 648559 Location: Clayton campus
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largely uses traditionally approaches when it comes to laboratory work and synthesis of materials, also here the advent of automation, robotics and machine learning is bringing tremendous change
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strong background in artificial intelligence, machine learning, and data analysis. Additionally, experience in healthcare informatics, user experience research, and a commitment to improving mental health
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, computer science, computer vision, or a related domain (a background in medical imaging is advantageous) Proficiency in Python programming or familiarity with deep learning frameworks like PyTorch
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three main projects. (1) The recruited PhD student will perform natural language processing and machine learning research into AI-augmented coaching to provide a pre-coaching session AI-based question and
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The objective of this project is to use machine learning techniques to help with the drug discovery by modelling structural and sequential data. This project is supported by a supervision team with
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challenging for clinicians and pregnant women. Digital health records, advances in big data, machine learning and artificial intelligence methodologies, and novel data visualisation capabilities have opened up
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testing advanced particle detectors and electronics, learning the modern data analysis with either simulation or real data in COMET to search for the muon rare process
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world, and with world-class photonic facilities at Monash. "Quantum nanophotonic chip" "Multimode imaging through ultrathin meta-optics" "Advancing optical imaging with flat optics" "Machine-learning
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they bond in materials, but also develop transferable skills in scientific computing, data analysis and visualisation. "Machine learning for atomic-scale structure determination in thick nanostructures" (with