52 phd-studenship-in-computer-vision-and-machine-learning positions at Chalmers University of Technology in Sweden
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). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction solvers. Computational aeroacoustics. Swedish is not required
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of computational fluid dynamics (CFD). Knowledge of finite element method (FEM). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction
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disciplines learn together. Our team is diverse and comprises researchers and teachers with architectural, planning, urbanism, and human geography backgrounds, Doctoral students, and other early-career
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, you must hold a PhD (awarded no more than three years prior to the application deadline*) in computer science, maritime transportation, or a related field, with a strong foundation in mathematics and
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aimed at building a high-performance quantum computer based on superconducting circuits. Our team includes a dynamic mix of PhD students, postdocs, and senior researchers working collaboratively
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-scale computational methods, and bioinformatics. The division is also expanding in the area of data science and machine learning. Our department continuously strives to be an attractive employer. Equality
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fluids, flow-induced pattern formation in both simple and complex flows (e.g. flow instabilities, product defects), multiscale analysis, and the application of machine learning techniques. About the
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facilities that are highly aligned with the goals of the project. Who we are looking for We seek candidates with the following qualifications: To qualify for the position of postdoc, you must have a PhD degree
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about working at Chalmers and our benefits for employees. The position is limited to four years, with the possibility to teach up to 20%, which extends the position to five years. Doctoral studies
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to develop solutions with real world relevance and impact. This project will be carried out in close collaboration with researchers from the Division of Material and Computational Mechanics at IMS and the