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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Edinburgh, Scotland | United Kingdom | 3 months ago
technologies tailored to CCUS needs. Accurate knowledge of CO2 streams density is vital across the CCUS chain, impacting engineering design, pipeline safety, reservoir management, and financial settlements
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knowledge of their validity through practical measurements in real conditions. The academic supervision team have a track record of more than 20 years each on multistatic radar research and some 500
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• To liaise with research colleagues and make internal and external contacts to develop knowledge and understanding to form relationships for future research collaboration • To plan and manage own research
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a PhD/DPhil (or have submitted a thesis) in a field such as medieval studies, book history, or digital humanities. You will have excellent reading knowledge of Latin and expertise in manuscript
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analysis. Candidates are also expected to bring their own knowledge and approaches to design and execution of the project. The candidate will work in a dynamic team of scientists and collaborate with group
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of research The Person (Essential) Knowledge, Skills and Experience • Have expertise using ‘display’ screening techniques such as mRNA-display, phage display, DNA-encoded libraries or similar • Experience
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hold, or be close to completion of, a relevant PhD/DPhil, have relevant experience in the integration of social science methods and ethical analysis and possess sufficient specialist knowledge of ethics
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) opportunities for publication; (2) the chance to develop knowledge and skills in neuroimaging analysis; and (3) experience of working with digital mental health and smartphone-based assessment tools. There will
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(liquid nitrogen and/ helium atomic scale STEM Knowledge of in situ STEM characterisation Expertise in aberration corrected STEM What we can offer you: Grow your career: gain access to Imperial’s sector
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Appropriate computational skills and knowledge of programming languages (Python, C++, etc.) Experience with Machine and Deep Learning models and software (Keras, Scikit-Learn, Convolutional Neural Networks, etc