14 computational-physics Postdoctoral positions 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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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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(Physics, Applied Physics, Nanotechnology, Computer Science, Engineering, or equivalent), obtained no more than three years prior to the application deadline (according to the current agreement with
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The group of Sophie Weber (Condensed Matter and Materials Theory Division of Physics, Chalmers University of Technology) invites applications for a postdoctoral position on the first-principles
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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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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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of support for the recruiting process in connection with this position. *** Chalmers University of Technology in Gothenburg conducts research and education in technology and natural sciences at a high
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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