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correction. This machine-learning approach, however, needs a realistic model of light propagation in the retina in order to validate it and to generate the large volumes of training data required. Funding
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information about the role, please contact Prof. Radu State Your profile Strong background in AI, machine learning, or multi-agent systems, ideally with interest in financial systems, decentralized ledgers
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which there exists extensive experience in the areas of machine learning, biostatistics, and medicine: Dr Yanda Meng and Dr Tianjin Huang (Machine Learning), Prof Yalin Zheng (AI in Healthcare), A/Prof
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Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning
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of innovative computational methods using Big Data, Behavioural Science and Machine Learning to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging
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, analytical and computer programming skills. Advantage will be given to applicants with experience in one or more of the following: signal processing, deep learning, acoustics, psychoacoustics, acoustic
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, machine learning, and information-theoretic approaches to achieve robust, non-intrusive security for the ever-expanding IoT landscape. Feature Engineering for Encrypted Traffic: It is crucial to identify
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/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition, applicants should be highly motivated, able to work independently, as
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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nonlinear effects. These nonlinear effects will be generalised via correction terms discovered by machine learning from a large numerical simulated dataset. This dataset also allows for extending the theory