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of chemical engineering, process engineering, mechanical engineering, or a comparable and closely related field of study Knowledge of thermodynamics, heat and mass transfer phenomena, process engineering, and
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or Electronic Engineering, Biomedical Engineering, Chemical Engineering or related field Relevant professional experience in the specified area (synthesis, fabrication, characterisation) Background in functional
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Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally
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technology. The following PhD topics are available, which are explained in more detail by clicking on the topics (on rptu.de/wera ): In Chemistry: A3: Development of a flow process for P removal from
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PhD Position – High-Temperature Electrolysis – from stack design to operational optimizationFull PhD
journals and active exchange with industry and research partners Your Profile: Completed master's degree in chemistry, electrochemistry, technical chemistry, chemical engineering or a comparable field (e.g
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reinforcement tasks to work with, enhance or replace established methods from computational engineering and computer simulation (such as the finite element method) to represent and exploit relationships along
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improve the sustainability, the energy efficiency and the safety of industrial processes. The Department of Department Fluid Dynamics Resource Technology Processes is looking for a PhD Student (f/m/d
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medicine, immunotherapies, as well as cell and gene therapies. Our interdisciplinary environment brings together a broad range of expertise, including biomarker discovery and evaluation, chemical synthesis
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multidisciplinary educational background, and possess a Master's degree in physics, physical chemistry, chemical engineering, materials science, nanoscience, mechanical engineering, or a closely related discipline.
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project work plan and milestones Your profile Completed university studies (Master/Diploma) in the field of Chemical/Metallurgical/(Mineral) Process Engineering, Data Science, Statistics, Machine Learning