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Field
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models for structural health monitoring of civil engineering structures. Digital twin models are used to interpret real time information from videos and images aided by computer vision techniques
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tools. * Familiarity with tensorflow and/or pytorch. * Demonstrated ability to design and implement machine learning techniques and algorithms. * Demonstrated expertise in the Linux computing environment
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German language proficiency is not required. Applicants must provide proof of their English skills with one of the following certificates: TOEFL minimum of 550 (paper-based test), 213 (computer-based), 79 (Internet
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(immunohistochemistry and in situ hybridization) FACS and flow cytometry Proteomics analysis Computer image analysis We are looking for motivated candidates who hold a B.Sc. or MSc. in biology or related discipline. They
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experienced supervisors, each with over 20 years of expertise in machine learning and computer vision. These supervisors have strong track records of research excellence, with numerous publications in top-tier
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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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models. Your tasks: Research, development, and evaluation of Machine Learning and Deep Learning methods Prototype development Literature review Publication and presentation of scientific results in
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Machine Learning for Image Classification. Eligibility You must: We would like you to have: sound knowledge of machine learning, computer vision and image processing strong programming skills. How to apply
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journals. Close collaboration with team members and colleagues. Essential qualifications: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong
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: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong knowledge in Machine/Deep Learning with experience in discriminative models