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of reinforcement learning or agent-based systems. LanguagesENGLISHLevelExcellent Research FieldComputer science » Computer systemsYears of Research Experience1 - 4 Additional Information Benefits • Full funding
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-class or 2:1 (or international equivalent) Master’s degree in Computer Science, Robotics, Mechatronics or Electronic/Electrical Engineering, or a related field. • Knowledge of machine learning/deep
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monitoring and conservation applications, while Bristol offers advanced training in machine learning, spatiotemporal modelling and AI applications to animal behaviour. Together, they provide computational
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. Interested candidates may want to take a look at our recent work on machine learning molecular dynamics: https://www.nature.com/articles/s41467-024-52491-3 Project 2: Non-adiabatic Molecular Dynamics
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interest, narrowing the scope to natural or cultural sites, and integrating diverse remote sensing datasets. The supervisory team offers interdisciplinary expertise in geospatial analysis, machine learning
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implement more effective interventions based on up-to-date predictions. The ideal candidate will have foundational knowledge of machine learning and strong self-motivation. You will be supervised by Dr
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contribute significantly to these growing fields. This PhD position is ideal for candidates interested in the following areas of machine learning: Geometric learning: exploiting the structure of data (e.g
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that can be run. Emulating expensive processes could allow more data to be generated from better models, at lower cost. The central science question is: how can machine learning and evolutionary computation
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training dataset of well-studied volcanoes with known large eruptions, the project will employ statistical and machine learning (ML) methods to identify the strongest predictors of eruption magnitude
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multimodal satellite Earth Observation and machine learning can be used to quantify cyclone and storm damage in plantation forests. The core focus could be on integrating pre-storm LiDAR with post-storm