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Field
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consistently score highly in the National Student Survey in the Postgraduate Taught Experience Survey. About the role The post is an exciting opportunity for a researcher to develop novel statistical methods in
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a new computational paradigm that combines the versatility of the digital computer with the efficiency of close-to-physics computing. The group targets the full computational stack, from materials
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, or the wider region. Skills in material science and decay analysis, conservation practice, or digital-humanities methods (network analysis, GIS, computer vision). Familiarity with international
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methods for evaluating AI tools for public health applications; participate in the development and testing of AI-enabled analytical approaches, prototypes; examine how AI can help integrate and interpret
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demand across agricultural and urban land uses. The team will (1) develop and validate locally relevant methods to estimate consumptive use across mixed urban–agricultural landscapes, (2) estimate
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of the cryosphere and related data science methods: the European Research Council (ERC) Synergy Grant SnowShifts with University of Oslo (Prof. A. Kääb), EPFL Sion and WSL/SLF (Prof. M. Lehning), and BOKU Vienna (Dr
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30 Jul 2026 Job Information Organisation/Company Universidade de Coimbra Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Master Positions
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and gradient-based optimization methods in optimization of complex structures are significant. The knowledge of programming languages, numerical methods, material properties including their tribological
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outputs, and nowcasting model products for hazard prediction workflows. • Develop and apply machine learning–based post-processing methods to enhance forecast skill for convective hazards. • Perform
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Microsoft Office applications and familiarity with statistical analysis methods and software. Critical thinking and scientific judgment to evaluate complex environmental health data and site conditions to