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. The research combines robotics, computer vision, artificial intelligence, machine learning, control systems, and medical robotics to solve one of the most challenging problems in modern automation. Project
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applicants with a background or experience in: • Computational chemistry • Materials simulation • Scientific machine learning / AI • Molecular dynamics or DFT • Materials science or a related discipline
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with one of the following doctoral research projects. 1. AI-Aided Site Reconnaissance and Rapid Assessments for Settlement Upgrading How can spatial analytics, remote sensing, and machine learning models
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be used to combine these datasets while accounting for their different spatial scales, uncertainties and sampling frequencies. Machine-learning methods may also be explored for retrieval, bias
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the Department of Materials Science and Engineering within the Faculty of Engineering. Project title: Processing intelligence for green metals using in situ X-ray characterisation and machine learning. We
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University of Technology (QUT, Brisbane, Australia) related to machine learning for particle laden fluid mechanics. QUT is a major Australian university with a global outlook and a 'real world' focus. We
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should ideally have experience in: Essential Deep learning and machine learning Computer vision Python programming PyTorch or TensorFlow Strong mathematical and analytical skills Desirable Video
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and/or external study is obtained). Have an Honours or Master’s degree (or equivalent) in one of these areas: Engineering, Computer Science, Human Factors, Psychology, Data Science, Robotics, or a
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language processing, large language models, machine learning, network analysis, social media analytics, and large-scale analysis of online discourse and communities. This PhD scholarship will be based within
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microfluidic fabrication and experiments 3D printing machine learning. Demonstrated programming skills (Matlab, C++, or Python). Desired Demonstrated ability to work independently and to formulate and tackle