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Job Description DTU, Department of Civil and Mechanical Engineering, the Section for Manufacturing Engineering invites applications for a PhD position (3 years) on the topic of simulation of process
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and machine learning, you will help develop new methods for understanding complex failure mechanisms—an area where existing industrial knowledge remains limited. The project will be executed in three
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variable load and transient conditions. Develop a transient model to assess behavior during power demand fluctuations and optimize operational strategies. Study the degradation mechanisms of the HT-PEMFC
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, statistical mechanics and/or probabilistic machine learning. You should be willing to take an ambitious and unconventional approach to hard problems and be motivated by a desire for both deep understanding and
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of Civil and Mechanical Engineering, Thermal Energy Section. We look for a talented, self-motivated, and team-oriented individual who thrives in a collaborative environment and enjoys tackling complex topics
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robots will make current labour-intensive cultivation systems (e.g., stripcropping, targeted mechanical weed control, etc.) profitable, contributing to increased biodiversity locally and reducing the need
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- or nanoengineering, material science, mechanical engineering or similar. Experience with cleanroom fabrication processes or additive manufacturing is highly beneficial. You are expected to have a keen interest in
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operation mode can have a large influence on performance and lifetime. Understanding the degradation mechanisms and their dependence on operation parameters is therefore highly important, not only
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of electron microscopy imaging and spectroscopy to reveal the structure–property relationships that govern molecular adsorption mechanisms. This interdisciplinary project is fully funded by DTU’s PhD grant
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focused on development of novel methods involving organic chemistry, transition-metal catalysis, photocatalysis, enantioselective catalysis, and C-H functionalization as well as reaction mechanism