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Field
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expertise in nonlinear numerical simulation Reliability and risk assessment – probability of failure and its consequences, using state-of-the-art structural reliability methods and target reliabilities
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doctorate, contains: Artificial Neural Networks and Large Language Models offer state-of-the-art performance at numerous AI tasks, but being black boxes, they lack explainability which makes them difficult
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for admission to a PhD programme. Strong background in Computational Mechanics, Solid Mechanics and Numerical Methods. Knowledge of the Finite Element Method. Programming skills (Python & Fortran). Good written
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Master's degree in Materials Science and/or Computational Methods in Mechanical Engineering (or a closely related field). The successful candidate should demonstrate a strong background in numerical methods
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development (experience in multiple fields are considered beneficial). Familiarity with simulation software and numerical methods and proficiency in programming languages (Rust, Python, MATLAB, C/C
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lower level of technological maturity. Conventional cell concepts rely on ceramic support structures, which provide numerous advantages but also present drawbacks, particularly regarding their thermo
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/research/air-water-and-landscape-science ). Duties The PhD project will focus on uncertainty quantification in modelling flow and solute transport in fractured rocks. The work will include numerical
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on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
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the development of numerical methods for astorphysical fluid dynamics and radiation transport. Projects may employ a range of approaches from analytic modelling and numerical calculations on desktop
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., plasticity, damage, fracture) in engineering materials at different length scales, which emerges from the physics and mechanics of the underlying multi-phase microstructure. An integrated numerical