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at the department) distributed over the employment period. The 4th year is contingent on the qualifications of the candidate and the teaching needs of the department and will be decided by the head of department upon
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Responsibilities: Development of stochastic and analytical methods for nonlinear partial differential equations Implementation of relevant numerical experiments using deep learning algorithms Job Requirements: PhD
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for a 4th year, consisting of 25 % career-promoting work (e.g. teaching responsibilities at the department) distributed over the employment period. The 4th year is contingent on the qualifications
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. Awards will be finalised by July 2026. Supported Areas for START Engineering Energy Resilience: Power electronic converters for distributed energy resources and microgrids; High voltage DC transmission and
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. Material Optimization: Use optimization algorithms to design FGMs that meet demanding performance criteria like fatigue resistance and durability. Systems Integration: Apply a systems engineering approach to
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to optimize fluid flow, heat transfer, and current distribution within the electrolyzer. • Model key parameters such as overpotential, gas diffusion, ion transport, and pressure drop to improve overall
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the use of Fibre-Optic Distributed Temperature Sensing and geophysical borehole logging. Show proven experience in numerical heat & transport modelling. To be successful at shortlisting stage, please ensure
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the use of Fibre-Optic Distributed Temperature Sensing and geophysical borehole logging. Show proven experience in numerical heat & transport modelling. To be successful at shortlisting stage, please ensure
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electrical power distribution system. Prior knowledge on power system condition monitoring would be an advantage. Experience in project work would an advantage. Share this job Facebook Twitter LinkedIn Apply
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may include but are not limited to: algorithm and software development; application or development of computational or statistical methods; data analysis; modeling; statistics and machine learning