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collaboration. The successful candidate will be exposed to and trained in both low-level instrumental modeling, high-level component separation and cosmological parameter estimation, and high-performance
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, Hydrology, Geosciences, Environmental Science, Environmental Engineering, Civil Engineering, Mining Engineering, Environmental Modelling, Geoinformatics, Geospatial Data Science, Geology, Applied Computer
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apply together to develop the following research project : AI-based Online Prognostics Model for Remaining Useful Lifetime Estimation of Power Modules This postdoctoral position is part of a proposal to
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Faculdade de Ciências Médicas|NOVA Medical School da Universidade NOVA de Lisboa. | Portugal | about 2 months ago
months, with an estimated start date of April 1, 2026. The fellowship contract may be renewed for an equal or different period until the project ends, provided the eligibility requirements for its granting
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PhD Research Fellow in Theoretical and Computational Active Matter Physics for Glioblastoma Invasion
data-driven modeling, parameter estimation, or model calibration Familiarity with high-performance computing or large-scale simulations Interest in close collaboration with experimental researchers
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; e) Certified technical-professional training related to the areas listed above; f) Experience in delivering oral or poster communications at scientific conferences; g) Basic computer skills from a
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statistical physics, solid mechanics, or fluid mechanics Experience with data-driven modeling, parameter estimation, or model calibration Familiarity with high-performance computing or large-scale simulations
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PhD Research Fellow in Theoretical and Computational Active Matter Physics for Glioblastoma Invasion
programming skills Desired qualifications: Experience in statistical physics, solid mechanics, or fluid mechanics Experience with data-driven modeling, parameter estimation, or model calibration Familiarity
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physics Solid programming skills Desired qualifications: Experience in statistical physics, solid mechanics, or fluid mechanics Experience with data-driven modeling, parameter estimation, or model
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conducting the following activities: - Analyze the sensitivity of the seismic hazard model to parameters such as ground motion prediction equations (GMPEs) and site effects. - Investigate differences between