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engineering, and have a strong background in numerical analysis and scientific computing. Required qualifications include basic knowledge of numerical methods for PDEs, expertise in High Performance Computing
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, bacteriophages. Prof. Hauryliuk obtained his PhD in 2008 at Uppsala University, Sweden. His scientific contributions were recognized though the Ragnar Söderberg fellowship in Medicine (2014), the Swedish Fernström
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-driven methods for structural analysis under multiphysics loading”. Expected start date and duration of employment This is a 4–year position from September 1st, 2025, or as soon possible. Job description
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live in. Your role Research in one or more of the following topics: complex analysis, real analysis, functional equations Teaching activity (up to 90 hours/year), tutoring of students, possible
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until 31/12/2025 Workload 1 607 hours per annum Mission development of predictive models for numerical simulation of material processing Principal activities Development of numerical tools for simulation
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, and academic institutions. Required Qualifications PhD in one of the following fields: smart urban systems engineering, computer science applied to urban infrastructure, data science, or a related field
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expected that the successful candidate will contribute to the computational analysis of multi-omic data that will be generated during the project. The candidate must hold or be near completion to a PhD/DPhil
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analysis of the genesis of norms in the political model of the Lao People's Republic, especially in the development of the new constitution and new codifications in criminal law Support in developing a
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of mechanical and physical properties of thin carbon/boron-nitride nanotube (CNT/BNNT) films. The goal of the project is to calibrate an existing numerical model and reproduce the realistic mechanical properties
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processes for data standardization, data warehousing, quality assurance development and data analysis. A successful candidate will work closely in different aspects of the quality assurance and data