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GPUs). Research Associate: Hold a PhD in high performance computing, computational fluid dynamics or a closely related discipline*, or equivalent research, industrial or commercial experience. Research
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computational fluid dynamics Experience collaborating with industry partners and diverse research teams Creative problem-solving skills and ability to work independently What we can offer you: Join an exciting
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Mathjobs. Group Introduction: Alexey Cheskidov's research group will study some fundamental problems arising in mathematical fluid dynamics and turbulence, including regularity properties of solutions
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 10 days ago
employs about 300 people. We are part of the Göttingen Campus, a collaboration between the university and non-university research institutions. The Laboratory for Fluid Physics, Pattern Formation and
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 7 days ago
Job Code: MPIDS-W086 Job Offer from June 23, 2025 We seek to fill one position in the research group on Turbulence and Particles in Fluids lead by Dr. Gholamhossein Bagheri and in collaboration with
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) A PhD in a related discipline, and/or relevant work experience. Experience in at least two of the following areas: engineering design, thermodynamics calculations, heat transfer, fluid flow, radiation
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have a PhD in environmental engineering, earth or environmental engineering, or related fields, with a background in ecohydrology. Experience in ecohydrological modeling and remote sensing is desired
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phenomena Create new reduced-order models and submodels related to fluid flow, heat transfer, thermochemistry, and electrochemistry in multiphase systems Use modeling tools such as computational fluid
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candidate will play a key role in developing and advancing new models and simulations for Computational Fluid Dynamics (CFD) hypersonic codes. Specific tasks include developing new turbulence and transition
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cohorts of neurodegenerative disorders, which include clinical, neuroimaging, and fluid biomarker phenotypes. Characterization of the genetic topography of Alzheimer's disease (AD) provides a unique