15 parallel-and-distributed-computing-"UNIS" Postdoctoral research jobs at Chalmers University of Technology in Sweden
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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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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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We invite applications for several postdoctoral research positions in experimental quantum computing with superconducting circuits. You will work in the stimulating research environment
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of managing reverse logistics for returned products, and the potential disruptions to existing distribution networks. About us and the research project The division of Supply & Operations Management (SOM
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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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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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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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This postdoc project aims to address a critical challenge in quantum computing: errors in superconducting qubits caused by cosmic radiation, which cannot be corrected using existing methods
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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