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and research in several areas. These include, but are not limited to: Adversarial location and network interdiction models Adversarial machine learning attacks and defense (e.g., against Bayesian
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machine-learning, predictive-modelling or multivariate methods to behavioural and/or EEG data. Proficiency in R, MATLAB and Python. What we can offer In return we offer a generous pension, relocation
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or connectivity analysis; machine-learning or deep-learning methods for geospatial analyses; ecological or remote-sensing fieldwork, particularly in alpine environments; Google Earth Engine, geodatabases or cloud
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-scale inverse problems by combining interpretable high-level probabilistic models, multi-physics data integration, and modern machine learning. The resulting methods will be validated on groundwater
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Vision, Video Understanding, and Multimodal AI. Design AI models for concept-driven video understanding of consumer facial care behaviours. Work with PI and company to develop the AI solution Develop novel
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machine-learning models using large-scale aviation datasets. Analyse airport surface, runway, terminal airspace, and multi-airport system operations, including traffic-flow and delay-propagation mechanisms
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demonstrated expertise in AI and machine learning, including experience building and evaluating models in frameworks such as PyTorch, and running workflows in HPC environments experience applying machine
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proving, machine learning for reasoning, or neurosymbolic AI. Desirable Application/interview Further Information Grade Grade 8 Salary £48,822 - £51,753 Work arrangement Full-time Duration Until 30th
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to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural problems that also depend
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, machine learning, data-driven dynamics, stability/perturbation theory/rigorous numerics. Possible applications of the new theory include the analysis of spatiotemporal data arising from weather and climate