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Field
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-ray observatories, and involve complexity analysis on uniquely variable sources. Applicants with general experience related to these observatories, timing variability of compact sources, time domain
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developing advanced, technologically driven solutions for integrated monitoring and decision-making systems for complex structures and infrastructures. We invite applications for a postdoctoral research
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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control
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Neuroscience (IoPPN). The centre aims to better understand the complex interrelationships between society and mental health, with a commitment to: Ensure that the impact of social context is central to how we
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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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computations relevant to the development of strategic nuclear performance codes for nuclear reactors. This position resides in the Radiation Effects and Microstructural Analysis Group (REMAG) in the Materials in
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to the Institute for Condensed Matter and Complex Systems (ICMCS) of the University of Edinburgh. The appointed Postdoctoral Research Associate (PDRA) will utilise the laser-heated diamond anvil cell approach to
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papers. Requirements: Education: PhD in Physics or related degree Knowledge: Complex oxides (Ferroelectrics, antiferroelectric, nickelates, etc), nanomechanics, photovoltaics, scanning force microscopy
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-scale screens to study fundamental principles in molecular and complex trait genetics using microbes as model systems. Our core technology MAGESTIC (https://doi.org/10.1038/nbt.4137 ), a CRISPR/Cas9-based
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to the regulation of complex behaviours. This will involve a range of techniques including high resolution confocal microscopy to determine receptor localisation, behavioural analysis of C. elegans and computational