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Field
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theoretical analysis and numerical simulations, drawing on tools from quantum parameter estimation and quantum information, to analyse quantum-enhanced temporal measurement protocols, quantify the precision
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machine learning (including deep learning and/or reinforcement learning) numerical simulation of quantum dynamics Proven programming skills (e.g., Python/Julia/C++), including experience with scientific
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the documents as stated in "How to apply". Excellent written and oral proficiency in English Applicants with backgrounds in computational mathematics, numerical analysis, applied and computational algebra
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workflows, author high-impact peer-reviewed publications, deliver technical presentations, and collaborate closely with numerical modeling team members to upscale laboratory findings. Required Qualifications
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/ open quantum systems quantum control / optimal control machine learning (including deep learning and/or reinforcement learning) numerical simulation of quantum dynamics Proven programming skills (e.g
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) . The section engages in theoretical, experimental and numerical fluid mechanics. This includes technology for the green transition, marine hydrodynamics, hydrodynamic waves, and continuum mechanics. This project
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aerodynamic) and structural integrity analysis, primarily using numerical methods and surrogate models. The candidate is expected to work collaboratively in an international research environment. The research
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well as world leading industry partners. The candidate will be affiliated with the Mechanics Section (Section 1) . The section engages in theoretical, experimental and numerical fluid mechanics. This includes
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Norwegian AI centre funded by the Research Council of Norway (2025-2030). It comprises more than 200 faculty members from many higher education institutions in Norway, in collaboration with numerous public
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spatiotemporal decompositions of large high-resolution simulation datasets. • Develop and train machine learning architectures using reduced-order predictions together with heterogeneous observational data