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: PhD in solar energy, electrical engineering, or environmental sciences. Proficiency in PV systems, instrumentation, and performance measurement. Experience in processing environmental data (Python, R
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environmental data analysis. Familiarity with theoretical and practical aspects of scientific deep learning; Proficiency in programming languages such as Python and R. Strong experience working with climate and
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programming languages, especially MATLAB and Python. Excellent written and verbal communication skills in English. Application Applications should be submitted online and through email as a zipped file
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for data science (Python, R, SQL, PostGIS, GeoPandas, etc.). Knowledge of urban models and spatial analysis tools (urban growth models, accessibility, change detection, etc.). Ability to work with
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sciences, or related fields Strong background in hydrology and remote sensing techniques. Proficiency in computer programming tools such as Matlab and/or Python. Knowledge of statistics and mathematical
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of field campaigns, data collection and lab work, Spectral data analysis, data processing, and model development, ‘R’, Python programming / package development, Co-supervise PhD and undergraduate students
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in programming (Python, Julia) (provide evidence with specific examples). Experience with statistical modelling and experimental design. Ability to work in a multidisciplinary team. Strong written and
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Python programming and familiarity with ML frameworks such as TensorFlow, PyTorch, or JAX. Experience with cheminformatics tools (e.g., RDKit, Open Babel) and chemical reaction databases (e.g., Reaxys
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machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Hands-on expertise in programming languages such as Python, R, or MATLAB. Solid understanding of battery systems, electrochemical
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publication record in high impact conferences / journals. Very good programming skills (e.g., C, C++, Python), familiarity with Linux. Proficiency in English and ability to work in a team. Outstanding