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, or job experience. Proficiency in programming (Python, Julia) (provide evidence with specific examples). Experience with statistical modelling and experimental design. Ability to work in a
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modeling, satellite remote sensing, or machine learning Programming skills (Python or R) Strong publication record
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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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(e.g., Bioconductor, Galaxy, KEGG, Reactome, STRING). Proficiency in Python, R, and Unix/Linux-based environments for high-performance data analysis. Knowledge of biological network inference, causal
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of AI and Data Science : Machine and deep learning, NLP, BDI (Belief-desire-intention) systems, and Large Language Models (LLMs). Expertise in design and very good programming skills (Python, Pytorch
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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