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) Country Sweden Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a
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) Country Sweden Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a
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substantially better performance as compared to traditional power electronic devices based on Si and SiC. The position is part of a large initiative funded by KAW to develop a comprehensive platform that will
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of computer science and Media technology, Faculty of Technology and Society. Subject area The Research Assistant is to support a research project developing a machine learning-driven multimodal performance monitoring
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. Experience with high-performance computing clusters or cloud-based Earth observation platforms (e.g., GEE Python API). Experience with airborne lidar data processing and canopy height modelling. We will place
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We are offering a postdoc position in an exciting national project focusing on software security, with great opportunities for collaborations with top researchers in cybersecurity, program analysis
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collaborate with Doctoral students and postdocs working on similar topics! About us The Department of Computer Science and Engineering , a joint department of Chalmers and the University of Gothenburg, spans
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control engineering at both undergraduate and graduate level at Chalmers, including within the master’s programme Systems, Control and Mechatronics. About the research project High-power charging is
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without revealing to the server anything about the user data or even what kind of computations the user is performing. The project will explore the applications of FHE (Fully Homomorphic Encryption) towards
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, or reinforcement learning. Experience with high-performance computing (HPC). Experience supervising students or junior researchers. What you will do As a postdoctoral researcher, you will: Develop machine-learning