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project The successful candidate carry out will research in the field of theoretical continuous-variable quantum computation. In particular, the focus will be on bosonic codes, classical simulation
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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
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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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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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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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Are you passionate about sustainable biotechnology and ready to tackle one of today’s biggest environmental challenges? Join our dynamic and interdisciplinary team at Chalmers University of Technology to pioneer the next generation of biorefineries using marine and terrestrial biomass. This...
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-scale computational methods, and bioinformatics. The division is also expanding in the area of data science and machine learning. Our department continuously strives to be an attractive employer. Equality
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activities, which also include responsibility for the master's programme in Naval Architecture and Ocean Engineering and contributions to the education of seafarers. Your profile To qualify for this position
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, computational materials science, computer science, or a related field, awarded no more than three years prior to the application deadline*. Background in physics-based battery modelling and/or machine learning is
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to develop solutions with real world relevance and impact. This project will be carried out in close collaboration with researchers from the Division of Material and Computational Mechanics at IMS and the