49 parallel-and-distributed-computing-"Meta"-"Meta" positions at Chalmers University of Technology in Sweden
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challenges, and we are currently moving the code to a new python based High Performance Computing enabled modelling framework. This is an exciting opportunity to contribute to a high-impact scientific codebase
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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courses each year. We also have extensive national and international collaborations with academia, industry and society. This position is funded by the WASP program, and as such comes exciting career
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(masterexamen) of 120 credits or a Master’s degree (magisterexamen) of 60 credits in Electrical Engineering, Communication Engineering, Engineering Physics, Computer Engineering or similar, with a strong
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failures. We offer access to unique experimental data and computational tools developed by our research team for addressing a timely societally relevant problem. Project overview The aim is to unravel
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cladding materials. We will develop a full methodology for computational design, robotic 3D printing, assembly and disassembly of the panels, and demonstrate their application in typical insulated wall frame
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We are looking for a highly motivated, skilled, and persistent PhD student with experience in computational fluid dynamics (CFD) and some knowledge in structural analysis. The research aims
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to individuals who are currently enrolled in a master’s program at Chalmers University of Technology. Essential personal attributes include curiosity, persistence, and a learning eagerness. In addition, valuable
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%) Complete coursework relevant to the PhD program Your profile Required qualifications MSc degree in Environmental Engineering, Chemical Engineering, Civil Engineering, Biotechnology, or a related field
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in computer science, mathematics, statistics, bioinformatics, or equivalent. The candidate should have previous experience in bacterial genomics, machine learning/artificial intelligence, preferably