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scientific imaging. The primary responsibility of this position is designing and implementing sparse algorithms for large-scale scientific and numerical computations. The successful candidate will pursue
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on the development, theoretical analysis, and optimal implementation of numerical methods for systems governed by multiphysics and multiscale differential models, including the use of parallel computing architectures
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of experience within the field. Scientific and numerical programming in Python or R and knowledge of class ML methods and deep learning methods. Well versed in a variety of bioinformatic analyses, including Next
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these areas commensurate with career stage. Experience of FORTRAN and python programming. Previous experience in developing and using parallel computer codes using the Message Passing Interface (MPI). This is
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the application deadline. Have a solid background in quantum computing and quantum algorithms, sufficient to conduct research on the topic. Have programming skills (e.g., Python) and experience with numerical
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modeling with an understanding of numerical methods and physical processes relevant to ocean circulation and dynamics. Experience with high-performance computing, parallel numerical models, large scientific
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field. Candidates should have expertise in several of the following areas: Scientific software development (C++, Julia, Rust, Python, or similar) Numerical methods and scientific computing. High
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responsible for developing and analysing numerical models that describe progressive damage in laminated composites, with a clear emphasis on transferring detailed fracture mechanisms to models that can be used
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institution is its access to high-performance computing infrastructure, which is essential for large-scale numerical simulations. Researchers on the OPERA team regularly participate in collaborative projects
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, or related areas. Strong programming and numerical modelling skills using Python, MATLAB/Simulink, Julia, hardware in the loop implementation or comparable tools. Experience with one or more relevant methods