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Code scripts using programming languages such as Fortran, Python, and/or MATLAB Validate and test of new methods and perform numerical simulations Pose appropriate initial and boundary conditions for
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Transform/pseudospectral method for the development of numerical models Code scripts using programming languages such as Fortran, Python, and/or MATLAB Validate and test of new methods and perform numerical
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that are commonly used in the group include Python, high-performance computing, MPI, OpenMP, Commander, Git, and LaTeX. Familiarity with some of these concepts will be an asset, but is not required at the time of
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ICT systems’ security Strong programming skills, especially in Python, MATLAB, and/or C/C++. To be admitted into the PhD programme in Engineering and Science, the applicant must hold a master's degree
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in coding e.g. Python, or MATLAB Writing skills for research papers. Experience in applied machine learning, fault diagnosis, laboratory testing and development is a plus. Good knowledge in Norwegian
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mathematics, and finite element analysis. Programming experience in coding e.g. Python, or MATLAB Writing skills for research papers. Experience in applied machine learning, fault diagnosis, laboratory testing
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criteria Experience with programming or scripting (e.g., Python, R, SQL) for spatial analysis or modelling Experience with participatory planning tools or civic technology platforms A strong record of peer
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/BCR sequencing) and familiarity with R or Python are highly desired. Personal qualities: We seek a candidate who is intellectually curious, creative, resilient, meticulous in their research, and has a
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placing the results in a relevant clinical context that you have good programming skills in Python (PyTorch/TensorFlow) and are proficient in programming in Linux/shell that you are familiar with common
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professionally relevant background in algorithms, machine learning, database systems, or data mining. Very good programming skills using Python and/or Julia. Have excellent skills in mathematics and probabilistic