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Education Institutions. Preference factors: - Knowledge of fundamental concepts related to energy management and gas networks; - Knowledge of optimization and forecasting models; - Knowledge of Python
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); - Excellent performance in programming, software development course units.; Minimum requirements: - Proven experience with the Python 3+ programming language; - Experience with the TypesScript programming
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languages, including Python, Go, and Rust; - Experience with observability tools.; Minimum requirements: - Solid Knowledge of operating systems, including kernel structures; - Proficiency in the C programming
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. • Knowledge of the C++ and Python programming languages. • Experience with the scikit-learn, Yolo, and torchvision libraries/methods. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection criteria and
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for DL training.; - Knowledge in operating systems and distributed systems.; - Experience with Python, C++, and Go programming languages.; Minimum requirements: Experience in designing energy monitoring
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similar area The awarding of the fellowship is dependent on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions. Preference factors: - Knowledge of Python
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of Python programming. - Development of IT tools for use in real environments. - Knowledge of simulation methodologies. Minimum requirements: Past experience (or academic training) in Artificial Intelligence
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: Advanced knowledge of a programming language (e.g. Python, C++). 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection criteria and corresponding valuation: the first phase comprises the Academic
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integration and thermal networks; • proficiency in Python; Minimum requirements: - knowledge in programming; - fluency in English and Portuguese (written and spoken). 5. EVALUATION OF APPLICATIONS AND
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and collaboratively.; - Fluency in English (spoken and written); - Fluency in Portuguese (spoken and written). Minimum requirements: - Knowledge of programming in Python, R and C; 5. EVALUATION