574 postdoc-in-distributed-systems-and-controls-"Multiple" PhD positions in United Kingdom
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are evaluated in controlled settings and do not fully capture the realities of large-scale ecological applications. This PhD project will investigate Long-Tailed Open-Ended Semantic Segmentation (LTOESS), a
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previous experience of successful partnership working with multiple stakeholders, alongside academic research experience, is essential. Details of funding The successful applicant will: Be awarded an annual
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This PhD project is at the intersection of electromagnetism, numerical methods, and high-performance parallel computing, with application towards the design and optimisation of integrated circuits
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local gas/liquid phase conditions. Whilst direct simulations of breakup are possible, computational cost is high, restricting applications to small sections of geometry and for modest run times
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Applications are invited for a fully funded fixed-term position at the Research Associate (PostDoc) level in de-risking cirrus modification. Cirrus cloud modification (CCM) could in-theory mitigate
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vapour deposition (CVD) remains a leading method for CNT synthesis, yet the role of catalyst composition in controlling CNT morphology and purity is still not fully understood. This PhD project, in
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, researchers, patients and activists have formed around environmental sustainability in healthcare; in many instances these span multiple professional groups and organisations. Further research is now needed to
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at the University of Oxford has world leading expertise in flow and temperature control and cooling technologies. The research will study systems and components that will minimize any thermodynamic penalties
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applicant will ideally have previous experience of successful partnership working with multiple stakeholders, alongside academic research experience and evidence translation, is essential. The successful
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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in