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issues. Proficiency in urban modeling tools such as MATLAB, Python (especially libraries like Pandas, NumPy, SciPy, GeoPandas, etc.), and R. Advanced skills in predictive modeling and machine learning
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conferences (e.g., NeurIPS, ICML, ACL, EMNLP, etc.). Proficiency in programming languages such as Python, and experience with deep learning frameworks like TensorFlow, PyTorch, or JAX. In-depth understanding
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quality (PM2.5, CO2, Nox, etc.) and energy monitoring (voltages, currents, battery charge/discharge cycles); Proficiency in languages commonly used in IoT, such as C/C++, Python, and JavaScript. Expertise
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, Environmental Science, Remote Sensing, or related field Experience in atmospheric modeling, satellite remote sensing, or machine learning Programming skills (Python or R) Strong publication record Where to apply
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methods of soil characterization, monitoring and management, Organization of field campaigns, data collection and lab work, Spectral data analysis, data processing, and model development, ‘R’, Python
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. • Strong proficiency in Python and relevant libraries for data analysis and modelling (e.g., TensorFlow, Keras, Scikit-learn, Pandas); knowledge of R would be an asset. • Familiarity with geospatial data
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Engine. • Programming skills in Python or similar scientific computing environments, with experience in geospatial and machine learning libraries. SKILLS AND QUALITIES • Strong scientific rigor and
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the use of bioinformatics tools for metagenomic and transcriptomic data analysis (e.g., QIIME, DADA2, R, Python). Demonstrated ability to independently design and conduct experiments, analyze data, and
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programming skills (e.g., C, C++, Python), familiarity with Linux Proficiency in English and ability to work in a team Outstanding analytical and problem-solving skills Employment terms: The successful
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with material characterization such as XRD, SEM, FTIR, ICP-OES Very good programming and user skills in Python, MATLAB, LABVIEW or comparable technical computing and simulation environments Good English