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candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods for engineering and will use this experience in collaboration with
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heat and mass transfer, thermal comfort, and computational fluid dynamics (CFD). These skills will be applied to design, evaluate, and optimise strategies that enable the effective implementation of MMV
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turbine engines. Successful candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods for engineering and will use
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the next generation of gas turbine engines. Successful candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods
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energy modelling, CFD modelling, BIM and embodied carbon/lifecycle assessment for built environment applications is preferred. Knowledge in EN 15978 or similar standards for carbon assessment or lifecycle
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scientific articles for publication in leading peer-reviewed journals. Candidates must hold, or be about to obtain, a PhD in engineering, applied mathematics or a related subject area, with a major component
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scientific articles for publication in leading peer-reviewed journals. Candidates must hold, or be about to obtain, a PhD in engineering, applied mathematics or a related subject area, with a major component
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legislation. Procedure Reference: IT137-25-329 I - Legal admission requirements I.I - Grant Recipients: Masters in Mechanical Engineering, enrolled in the PhD Program in Mechanical Engineering. Knowledge in
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PhD Research Fellow in Hydrogen Safety:Hydrogen Explosions in Complex Geometries Apply for this job See advertisement UiB - Knowledge that shapes society Through robust and close interaction with
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imposed timelines/milestones. PhD (or near completion) in Engineering or Applied Science with strong expertise in CFD. Proficiency with open-source engineering software and numerical methods such as FEM