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on leveraging machine learning and generative AI techniques for proactive mental health monitoring. The project comprises three primary research packages that address distinct aspects of the overall goal
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machine learning to identify meteorites in aerial imagery, with great success, and recovered multiple meteorites using a UAV. This project will build on this success, by extending the drone approach to new
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on leveraging advanced computer vision (CV) and deep learning (DL) techniques to develop a state-of-the-art computer-aided detection and diagnosis (CAD) system for early-stage Alzheimer's disease (AD) detection
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intelligence, Machine learning, and Computer vision for subsurface characterisation This project aims to develop computer vision applications for geological characterisation using state-of-the-art AI/ML methods
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, Machine Learning, and Software Engineering. The HPIS group consists of leading computer science experts and works closely with different industries, e.g., Australian Defence, Governments, Software
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team of collaborators and stakeholders with expertise in biomechanics, spatial and speech sciences, computer vision, machine learning, and clinical experience in the diagnosis/management of Speech Sound
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with your current location and preferences. Our project aims to create an intelligent recommender system that seamlessly integrates geospatial data, leveraging advanced algorithms and machine learning
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courses Eligibility criteria Cybersecurity Knowledge, Coding, Network and Communication Protocols, Familiarity with Autonomous Vehicle Systems, Data Analysis and Machine Learning, Continuous Learning
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or good Masters in Biomedical Engineering or Computer Science. The applicant must have a willingness to learn multi-disciplinary topics and have a passion for research. Prior experience in writing their own
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publications and research experiences in structural dynamics and structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data