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seek a proactive and enthusiastic individual with a first-class or upper second-class honours degree (or equivalent) in: Engineering (Mechanical, Materials, or related disciplines) Physics, Chemistry, or
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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had
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should have or expect to achieve, at least a 2:1 (or equivalent) in any engineering degree programme, physics or mathematics. English language requirements: Applicants must meet the minimum
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/physics/biology) or engineering. The ideal candidate should have some understanding in the areas of Materials Science, Chemistry, Physics, Metallurgy, or Mechanical Engineering. The candidate should be self
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with participants to deliver measures concerning people’s experiences of the diagnostic process, their access to services and their general well-being and life quality. The positions are ideal for people
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methods for nuclear fusion, motivated by yield prediction in tritium fuel cycles. The lack of scalable tools necessitates large engineering tolerances, increasing reactor cost. Empirical tests are expensive
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Engineering, Chemistry, or related physical sciences related discipline. To apply: send covering letter, CV and academic transcripts by email to Geoffrey.Rivers@nottingham.ac.uk . Applications without academic
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composition and surface texture to wetting, and (4) investigate how plants can make surfaces with contrasting wetting properties within the same organ. The student will employ diverse techniques such as contact
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plants. Members of all three groups utilize highly wettable, wetness-'activated' slippery surfaces to catch insect prey in modified, cup-shaped leaves. The unusual wettability of these surfaces is based
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on the page https://www.nottingham.ac.uk/physics/studywithus/postgraduate/howtoapply.aspx In the “Research Proposal Section” of the online application simply state that you are applying to the open position