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manufacturing and additive manufacturing. Requirements: Candidates should have a background in one of: engineering, materials science, chemistry, and be willing to learn new disciplines and innovate to achieve
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information from high-quality videos that share content with distorted footage as constraints in the learning process of modelling algorithms. This method uses the characteristics and knowledge embedded in high
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pharmaceutical manufacturing and additive manufacturing. Requirements: Candidates should have a background in one of: engineering, materials science, physics, or chemistry, and be willing to learn new disciplines
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teach at the University of Vienna, where more than 7,500 academics thrive on curiosity in continuous exploration and help us better understand our world. Does this sound like you? Then join our
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of the grade as required by the programme PIs The Person Knowledge, Skills and Experience Ability to work well as part of a team and rapidly acquire new skills Detailed subject knowledge relevant to cancer
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-class honours degree or equivalent) in materials science, manufacturing, mechanical engineering, metallurgy, physics, chemistry, or related fields. Ideal candidates will be self-driven, eager to learn CFD
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Maguire. Training and Mentoring: You will learn a multidisciplinary set of practical skills including reaction kinetics, enzymatic chemistry, analytical techniques (e.g. NMR / fluorescence / UVVis
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Liverpool where, in the School of Computer Science and Informatics, we have an active group of PhD students, postdocs, and academics working at the intersection of Machine Learning, Verification and
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plan to test any device in the stratosphere. Teaching/learning support, networking and planning the use of resources also takes up a small portion of this position. The skills, qualifications and
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level. There is no plan to test any device in the stratosphere. Teaching/learning support, networking and planning the use of resources also takes up a small portion of this position. The skills