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Field
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which information to explore, and how different ways of structuring learning environments influence curiosity and learning. Computational models will be used to characterise individual differences in
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of treatments for a specific disease or condition can vary across individuals, so that in settings where multiple treatment options are available, different individuals may require different treatments to obtain
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exactly the immue system detects and responds to pre-cancereous lesions. Nevertheless, it is known that the activatin (or deactivation) of anti-cancer immune cells involves many different cell-types
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predict interaction effects. Unlike robot-specific neural network models, the proposed approach aims to learn a universal representation of local interactions (fluid-structure, robot-robot, robot-object
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central scientific challenge will be to learn integrated representations of forest ecosystems from datasets with very different characteristics, resolutions, coverage, and levels of supervision
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(EO) data can be used to assess different facets of ecosystem functioning in grasslands and forests. Within the project, you will collaborate closely with other PhDs and postdocs to collect field data
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will do Interpretable machine learning is a growing research area, with important applications in the biological sciences, such as understanding how different genes regulate each other within biological
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measures that are robust, reproducible and relevant to assessments used in patients and mammalian disease models. Your research will combine different approaches to: Develop and standardise behavioural
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to teach robots to understand forest well enough to navigate and move through them in real time, using machine learning on LiDAR point clouds and camera imagery for real-time understanding of the forest
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explore, and how different ways of structuring learning environments influence curiosity and learning. Computational models will be used to characterise individual differences in information-seeking