95 parallel-computing-numerical-methods-"Prof" positions at Chalmers University of Technology
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(R3) Country Sweden Application Deadline 15 Feb 2026 - 22:59 (UTC) Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU
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established groups in telecommunication, remote sensing, optoelectronics, quantum technology, and high-performance computing. About the division and department With more than 30 faculty members, more than 100
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staff position within a Research Infrastructure? No Offer Description Join our computational mechanics team at Chalmers University of Technology ! As a Doctoral student with us, you will develop numerical
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to join our dynamic, cross-diciplinary team. This recruitment is connected to a cross-diciplinary research project funded by the Swedish Research Counsil and executed in collaboration with Prof. Timur
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Division of Geoscience and Remote Sensing , we develop advanced methods and instruments to observe and understand the Earth system. Combining satellite, airborne and ground-based measurements with modelling
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such invariants behave under deformation and transformation, and to develop new tools that bridge geometric, analytic, and arithmetic perspectives. This project will be carried out in collaboration with Prof
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We invite applications for several PhD positions in experimental quantum computing with superconducting circuits. You will work in the stimulating research environment of the Wallenberg Centre
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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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. * Applicants who have not yet completed their Doctoral degree are welcome to apply, provided that the degree will be awarded no later than June 15, 2026. Has experience with computational methods, programming
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of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods