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Field
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teaching in model-based systems engineering from Loughborough Systems Engineering experts and industrial leaders. This PhD studentship is offered by the Model-Based Systems Engineering doctoral centre https
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student with an aerospace, mechanical engineering, or physics background. Experience of experimental and computational modelling of icing physics, instrumentation and imaging techniques would be
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Provide human experts with a reliable second opinion This project integrates image processing, data analytics, machine learning, and computational modelling, with applications in aerospace, mechanical
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collaboration, as part of the employment process at the University of Copenhagen. Notice: In the following description of the PhD programmes, some references are made to SCIENCE websites The PhD programme
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lives, appearing in processes such as in additive manufacturing, the Earth’s atmosphere, and inkjet/biomaterial printing. The key to understanding (and ultimately engineering) these applications is how
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electrical/mechanical engineering. Expertise in numerical electrical machine design tools (Ansys, JMAG, .etc) as well as corresponding scripting skills are desirable. Experience in electrical machine prototype
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per year for 3.5 years. Lead Supervisor’s full name & email address Dr Massimiliano Fasi: m.fasi@leeds.ac.uk Project summary The growing importance of artificial intelligence is fostering a paradigm
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, Attosecond science , Biological Physics , Biology , Biophysics , Biophysics/Soft Condensed Matter Theory , Biotechnology , Casimir effect , Chemical Engineering , Chemical Physics , Chemistry , Coherent
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management skills. Candidates with laboratory skills and imaging are desired for this project. Must be eligible to enrol in PhD programs at Curtin. Application process Please send your CV, academic transcripts
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for the company, which will open new markets and opportunities. This PhD covers a wide range of skills and so it is essential the candidate has a 1st degree in electronics, computing or aerospace/space technology