17 component-labeling-cuda Postdoctoral positions at Chalmers University of Technology
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the role We are looking for a project coordinator for a research project on “Robust post-processing of additively manufactured components”. This is a Smart Advanced Manufacturing project with 13 project
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collaboration with the Multiscale Inorganic Materials group, both part of the Division of Energy and Materials at Chalmers . The two groups together comprise nine senior researchers and 27 PhD students and
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Functional Materials, 2024, 34, 2406875). This project will further develop this technique with a focus on aerospace applications such as lightning protection, de-icing, and sensing. This position is part of
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degradation to the molecular characterization of biomass polymers and their conversion into functional materials. As part of your role, you will also contribute to a Horizon Europe project focused
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the experimental groups at Chalmers, as well as with international collaborators The project will be carried at the division Applied Quantum Physics at Chalmers, and as a part of the Wallenberg Centre for Quantum
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This multidisciplinary position is part of a WASP NEST (Novelty, Excellence, Synergy, Teams) project focused on advancing generative models and perceptual understanding in computer vision. The
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developing a novel imaging and amperometry-based platform for research into neurological diseases. About us The Esbjörner lab belongs to the Division of Chemical Biology , which is part of the Department
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to increase catalyst activity and selectivity. The computational part of the project will investigate relevant reaction paths and evaluate spectroscopic signatures that can be compared to a parallel
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electromagnetic processing. The work involves close cooperation across several departments. You will have access to a well-equipped experimental environment and be part of a collaborative team dedicated to creating
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knowledge base. Main responsibilities include: Conduct benchmarking and further development of risk assessment models and components. Investigate the reliability of accident data, including cross-validation