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partners across Europe to deliver a world-class doctoral training programme in risk assessment, resilience engineering, and smart technologies. Its scientific vision targets: (1) the development of a
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multiple locations across several targeted intervals. These will form the basis for empirical calibrations that we will use to predict the hypothetical natural baseline of Earth’s climate, free from
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projects, including: The post-holder will run numerical models that simulate the dispersion of greenhouse gases through the atmosphere. These models will be used, in Bayesian inference frameworks
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presentations, response to therapy, disease progression and complication, and that further subclassification of diabetes into more homogeneous groups offers opportunities for tailored and targeted early treatment
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(solid-liquid) reactions, often involving variable viscosity; and the need to monitor several different properties in near real-time. The latter involved several orthogonal online monitoring methods
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create a closed loop pipeline able to rapidly design binders to any target and optimized for developability. The program is rooted in DALSA (DTU’s Arena for Life Science Automation), a new
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, kernel machines, decision trees and forests, neural networks, boosting and model aggregation, Bayesian inference and model selection, and variational inference. Practical and theoretical understanding
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an integrated interdisciplinary PhD programme that consists of three main components: 1. An interdisciplinary research project that combines concepts and methods from different fields. 2. A substantial programme
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level techniques will be used, including recovery surgical techniques optogenetics, chemogenetics and targeted silencing behavioural testing general histological techniques and immunocytochemistry
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300 electron microscope and the Leica THUNDER Imager EM cryo-CLEM microscope. This technology allows us to target events that happen deep in the cell so we can see more of the context of virions