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Field
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to undertake world-leading research in the design, integration and Edge-implementation/testing of multimodal machine learning models. Your experience in real-time implementation of federated AI and Edge-based
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that pose a threat to US agriculture and public health. Potential areas of research include genomic epidemiology, virus evolution, ecological surveillance, ecological modeling and modeling of viral
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physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or equivalent) in
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the appointment. The candidates should have a strong track record in at least one of the group’s research areas, preferably demonstrated by publications in high-impact venues. Experience with machine learning
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Radio Frequency (RF) Systems: Extensive knowledge on network management and control Software Defined Radio (SDR): Data Analysis and Processing Programming and Software Development AI/Machine Learning (AI
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Programming and Software Development AI/Machine Learning (AI/ML): Research and Publication Additional information For informal queries, please contact: Professor Dimitra Simeonidou: Dimitra.Simeonidou
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uses. To investigate feature-level just-noticeable difference modelling for machines to facilitate assessment and optimization. To formulate a comprehensive visual feature codec for machine uses
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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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modelling, satellite data assimilation, multivariate statistics, and machine learning. Prior experience with model and satellite products for mapping and understanding SM-dependent hazards (like floods
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Responsibilities: Conduct programming and software development for data management. Design and implement machine learning models for optimizing data management. Conduct experiments and evaluations of the designed