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. 1. exploring how probabilistic or machine learning methods could be employed to improve the inputs into large-scale agent-based simulations of urban mobility patterns, allowing for more realistic
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great broad-based research-intensive universities. Applications for the post are welcome in the general areas of Human-Computer Interaction (HCI), which complement existing strengths in the Glasgow
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quantitative methods, social network analysis, text analysis, Bayesian modelling, longitudinal analysis, causal inference, agent-based modelling, Machine Learning, or similar. Skills Essential: C1 Excellent
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to the field of research (e.g., 3D dynamic computer graphics, Machine Learning, AI, cognitive science, signal processing, computational modelling). C3 Knowledge of project-specific technical models, equipment
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Job Purpose To make a leading contribution to a project Machine Learning Accelerated TCAD Simulations working with Prof. Vihar Georgiev. The successful candidate will also be expected to contribute
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leveraging recent advances made in multiscale mechanics and machine learning. The candidates should have a PhD, or have equivalent experience, in Mechanical, Civil, Chemical, Material Engineering, Physics
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knowledge and skills in a similar or number of different roles For appointment at Grade 7: A2 A PhD or equivalent qualification/experience in statistics, machine learning or data analysis. Desirable B1
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research priorities. They have expertise in analysing next generation sequencing data, metagenomics, transcriptomics, data integration, molecular evolution and phylogenetics, machine learning, software
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. We are looking for someone with expertise in one or several of the following; integral projection models, movement ecology, population ecology, machine learning, or animal-borne telemetry
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statistics, as well as substantiated knowledge of machine-learning and Artificial Intelligence (AI) methods applied to Earth Observation geospatial data (e.g., satellite imagery, sensors, street view images