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with Linux, Docker, MongoDB, PostgreSQL, and Opal technologies; - Experience with CI/CD methodologies and technologies; - Software development experience based on JavaScript (ReactJS, NodeJS) and Python
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on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions. Preference factors: - Experience with different programming languages, including C, Python, Go, and Rust
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for spectral and spatial data pre-processing, as well as on machine learning algorithms, chemical map reconstruction, and algorithmic correction of galvanometric scanning.; Training will cover Python programming
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., Python and/or MATLAB) and code management tools. Good knowledge of written and spoken scientific English. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection criteria and corresponding valuation
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experience in a professional context; - Experience in developing software based on JavaScript (ReactJS, NodeJS) and Python; - Experience with the use, integration, and development of APIs; - Experience in
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learning. Experience in scientific programming (e.g., Python and/or MATLAB) and code management tools. Good knowledge of written and spoken scientific English. 5. EVALUATION OF APPLICATIONS AND SELECTION
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requirements: Experience in fluid-structure interaction, numerical modeling, dynamic analysis of offshore structures and/or processing of sensor data. Skills in scientific programming (Python, MATLAB
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Python and specifically PyTorch/Tensorflow; - Previous experience in the preparation and submission of scientific papers; - Previous experience in medical image research projects; - Proficiency in
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of mathematics, machine learning, proficiency in programming languages including Python, C/C++. Experience with machine learning. Excellent academic background. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS
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factors: The candidate should have:; • Strong knowledge of electrical power systems; • Experience with computational simulation software such as PSS/E and/or DigSilent; • Experience in Python and CPLEX