47 postdoc-in-postdoc-in-automation-and-control positions at Chalmers University of Technology
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magnetoelectric coupling, particularly those related to chiral magnetism. Who we are looking for We seek candidates with the following qualifications: To qualify for the position of postdoc, you must hold a
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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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characteristics, reuse, recycling, and overall sustainability across various materials and applications. Our AM group is one of the largest in Europe, bringing together dedicated researchers, postdocs, and PhD
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postdocs at Chalmers, and collaborate with academic and industrial partners in Sweden and internationally. The role also offers opportunities for travel and engagement with external collaborators. Research
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to develop a more sustainable and energy efficient method for recycling carbon fibre composites. The approach uses alternating magnetic fields to heat the material in a controlled way, breaking down
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parental leave, sick leave or military service. What you will do Your major responsibility as postdoc is to perform your own research in a research group. In this role, you will play a pivotal role in
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Technology Laboratory (QTL) division of the Microtechnology and Nanoscience (MC2) department, working in a large team of PhDs, postdocs and researchers. About the research We are seeking PhD students to work
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senior researchers, three postdocs and three PhD students. It is embedded in an interdisciplinary environment where we have close collaboration with other research teams at Chalmers such as technology
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, transduction, thermodynamics, and foundations. By controlling matter at the quantum level, we explore novel ways to process information beyond classical limits. QTLab offers an international, dynamic research
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developing AI methods for automated microstructure analysis and 3D microstructure generation. By combining self-supervised learning and diffusion-based generative models, the goal is to: Reconstruct high