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level. Background in solid-sate physics, semiconductor devices, quantum information, and/or analogue circuits is desirable. Experience in data analysis and programming, particularly in the use of Python
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Geográfica (SIG). - Dominio en modelización estadística de datos y de software estadístico y SIG (R, QGIS/ArcGIS, Python). - Experiencia en la redacción de textos científicos y buen nivel de comunicación
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in wireless networks, signal processing, and backscattering techniques. Understands 3GPP and UHF RFID protocol fundamentals Has strong programming skills (Python, C/C++, MATLAB). To accredit a level of
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/PhD) or related field. - Simulation & data: TRNSYS (or similar), time-series processing; Python (pandas/numpy). - Experience with GIS and/or climate/solar datasets (e.g., METEONORM, PVGIS
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imprescindible, se valorará positivamente en el proceso de selección la titulación de Máster y la experiencia/conocimiento en algunas de las siguientes áreas: lenguajes de programación (Python, JavaScript
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and scored: A. Knowledge of Matlab, Python, R and/or C++ (advanced level). These will be assessed based on accredited training hours (0.5 points for every 10 hours of training, up to 5 points for each
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Matlab, Python, R and/or C++ (advanced level). These will be assessed based on accredited training hours (0.5 points for every 10 hours of training, up to 5 points for each course) and accredited project
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biology and laboratory techniques (e.g., qPCR, immunofluorescence, among others). Knowledge of image analysis using ImageJ or similar software. Proficiency in MATLAB and Python will be considered
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Knowledge of Matlab, Python and software for signal interpretation Experience in laboratory for biomedical signal acquisition Experience in joint projects with Hospitals or Health institutes Scientific
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of this annex, as well as to: Programming in Python and R. Statistical classification and machine learning methods: SVM, neural networks and logistic regression. 3.2. Qualification: Official Master’s degree in