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Field
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Research Associate specialising in statistical modelling and machine learning to join our multi-university multi-disciplinary team developing a groundbreaking technique based on autofluorescence (AF) imaging
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) Development of an Augmented Smart Classroom for Personalized Learning (SmartClass) serving as a test-bed for the collection and analysis of students and professors data, leveraging on data analytics and machine
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presentation/publication of research findings. Candidates with a Biology and Biomedical Science related Master's degree or DVM degree in addition to a PhD are preferred. This position will require attention
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machine learning/AI or cybersecurity, or both, as evidenced by a strong track record of publications in leading journals and conferences in relevant areas. Software programming skills. To be successful
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
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with a PhD in computer science or bioinformatics are encouraged to apply. We create statistical, machine learning, and deep learning approaches for the processing of this data, with a major focus on
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. Specifically, the PhD candidate is expected to contribute corpora preparation (collection and organizing the annotation), use machine learning approaches for irony detection, and testing for experimental and
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postgraduate-level research in Computer Science, Cybersecurity, Information Security, Information Technology, Artificial Intelligence, Machine Learning, or a related field, have experience with securing AI
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) Corrosion behavior (electrochemistry & high-temperature oxidation) In-situ monitoring of AM processes Computational skills in: Phase-field modeling, Machine Learning, FEM, DEM, COMSOL Alloy design (CALPHAD
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learning. The post-holder will be familiar with the use of these techniques and experience of dataset construction and data mining will be essential. The successful applicant will have completed an MPhil/PhD