180 structures-"https:"-"https:"-"https:"-"https:"-"https:"-"Robert-Gordon-University" positions in Norway
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creative ability to work structured and handle a heavy workload having a good command of both oral and written English via Unsplash Requirements for competence in English A good proficiency in English is
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the timeframe ability to work independently and in a team, be innovative and creative ability to work structured and handle a heavy workload having a good command of both oral and written English via Unsplash
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with standard-grounded assurance principles into a coherent theoretical structure. The PhD candidate will conduct fundamental research on how monitoring information can be used to evaluate adaptive
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with tunnelling Be prepared for changes to your work duties after employment. Required selection criteria You must have a relevant master’s degree in construction engineering, engineering geology
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for completing the doctoral degree within the timeframe ability to work independently and in a team, be innovative and creative ability to work structured and handle a heavy workload having a good command of both
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diverse and impactful set of use cases: Logistics, shipping and business transactions; Ship design and construction; Voyage planning and optimization; Condition monitoring and maintenance; Crew training
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understanding how imaginaries, structures, and agency co-evolve across space and time, contributing to uneven geographies of future-making. The qualifying project will be carried out at the University
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and in a structured manner, with great attention to detail. Demonstrate very good collaborative and communication skills. Proficient in both written and oral English. You will be offered the opportunity
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the timeframe ability to work independently and in a team, be innovative and creative ability to work structured and handle a heavy workload having a good command of both oral and written English via Unsplash
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in English Solid knowledge in finite element analysis (FEA) and strong skills in FEA software such as ABAQUS Hands-on experience in the construction and application of deep learning neural networks