49 engineering-"https:"-"https:"-"https:"-"https:" Postdoctoral positions at Delft University of Technology (TU Delft)
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Join TUNED and develop the models that will enable engineers to design tunnels in deep clay formations - an essential step in the Netherlands' journey towards safe, long-term disposal of nuclear
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difference wave-equation solvers such as SeisSol or SPECFEM3D. Affinity with the advancement of geothermal energy. TU Delft (Delft University of Technology) Working at TU Delft means contributing to solutions
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join the Department of Hydraulic Engineering at TU Delft’s Faculty of Civil Engineering and Geosciences. The department conducts research and education in hydraulic, coastal, river and offshore
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. The project will be conducted within the Process and Energy Department at the faculty of Mechanical Engineering at TU Delft, under supervision of Dr. Mahinder Ramdin and Prof. Rene Pecnik. Job requirements The
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(there is room for learning-on-the-job). A PhD in aerospace/mechanical engineering or applied physics. Demonstrated ability to conduct research in experimental fluid mechanics. Proven competence on flow
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University of Technology) Working at TU Delft means contributing to solutions that really make a difference. For over 180 years, we have been training engineers who make an impact worldwide in companies
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science, atmospheric chemistry, environmental science, aerosol science, physics, environmental engineering or a closely related discipline. Demonstrated expertise in atmospheric chemistry, aerosol science
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. The ideal candidate holds a PhD in transport, operations research, civil engineering, or a related field, with strong skills in mathematical optimization and modelling (ideally in Python) and an interest in
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Use real-world vessel data, agent-based modelling and digital twin technology to accelerate the transition toward zero-emission inland shipping. Job description The postdoctoral research is part of
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, Mathematics, Bioengineering or a related discipline PhD in Physics, Applied Science, or a related discipline Experience in statistical physics, stochastic processes and/or data analysis methods Strong interest