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-energy perturbation. Experience with computational workflow tools and computing environments and high-performance computing. Exposure to workflow automation and collaborative software development practices. Courses in
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master’s thesis projects. Ability to secure external research funding in competitive calls. Very strong ability to work with high-performance computing (HPC) environments. The extent to which the applicant
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for Neural Rendering for Computer Graphics and Real-Time Rendering. By using ANNs, coded for high-performance on cross-vendor GPUs, we aim to create new techniques for global illumination and material models
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for retrieval of nanoscopic information from their surfaces, microstructural information from their gut content as well as relating insect abundance to CO2 and heat convection cells in the atmosphere. As a
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manager. The work duties also comprise establishing data analysis pipelines required for the analyses suitable for large scale analyses on high performance computing clusters. Moreover, the work includes
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are united in our efforts to understand, explain and improve our world and the human condition. Your Team You will join the Scientific Data Group of the Technical Division. We provide the beamlines with a high
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Doctoral student in development of nanowire devices for photonic neuromorphic computing (PA2026/472)
of writing a doctoral thesis. You are expected to participate in a research project, doctoral courses, seminars and conferences. The project takes its starting point in high performance nanostructures
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several industrial and academic partners. The candidate will work with high-performance computing resources (NAISS) and advanced CFD tools, primarily OpenFOAM, with the possibility to use Nek5000 or LBM
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test multi applications. Within this project we will design a ultrabroadband and a high spectral resolution hyperspectral lidar. The development is done by raytracing, Computer Aided Design and 3D
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Information Science (GIS), and computational science for health and environment, to study processes spanning from the microscopic to the planetary, across all time scales. The Inverse Modelling group at the Department