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This PhD position is part of the WASP-WISE NEST project RAM³ – a multidisciplinary research effort at the intersection of machine learning and materials science. The project brings together PhD
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-loss and highly decoupled FR3 (7-24 GHz) arrays, potentially based on three-dimensional elements and classical Huygen source type of element supporting element-specific band-selection filterig will be
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occupational traffic safety, with a focus on the car driving behavior of employees who drive as part of their job but are not professional drivers. Lack of knowledge, training, stress, fatigue, and poorly
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development for powder-based metal additive manufacturing (AM), covering the full value chain—from powder production and AM process development to post-processing and final components. Hosted by the Division
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Team up with up to 3 fellow Ph.D. students in the DSP-assisted Wideband & Efficient Transceivers (SWEET) project which is part of the WiTECH center to perform cutting-edge multi-disciplinary
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take part in research communication through co-authored articles in top-ranked academic journals and conferences, industry papers and exhibitions of architectural demonstrators and prototypes
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We are offering a PhD student position in machine learning (ML) theory, focusing on new methods for training models with a limited amount of data. The student will be a part of a new NEST initiative
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, circular processes, and sustainable development. It involves both new production and the transformation of already existing buildings. As a PhD student in this Division, you will be part of an excellent
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, Sweden. The student will form a part of a new NEST initiative funded by the Wallenberg Initiative Materials Science for Sustainability (WISE) and the Wallenberg AI, Autonomous Systems and Software Program
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full-time employment at the Department of Computer Science and Engineering at Chalmers University of Technology, and based in Gothenburg, Sweden. The student will form a part of a new NEST initiative