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at conferences, and stakeholder engagement sessions. Required Qualifications: A Ph.D. in Climate Science, Hydrology, Environmental Science, or a related field. Experience in machine learning or AI applications in
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like Pandas, NumPy, SciPy, GeoPandas, etc.), and R. • Advanced skills in predictive modeling and machine learning, particularly for multi-variable simulations. • Knowledge of complex systems
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and regional scales. Proficiency in programming (e.g., Python, R) and experience with machine learning for geospatial data analysis. A strong track record of publishing research articles in high-impact
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energy applications (domestic energy consumption, electric vehicles, smart buildings). • Proficiency in optimization techniques (mathematical algorithms, heuristics, or machine learning) applied
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machine learning, particularly deep learning and natural language processing. Experience with transformer-based architectures (e.g., BERT, GPT) is highly desirable. Proficiency in Python and relevant
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: Experience in developing smart city models focused on health and environmental infrastructures. • Advanced Data Analysis: Advanced skills in machine learning, deep learning, and advanced statistics
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Bioinformatics, Computational Biology, Computer Science, Genomics, or a related field. Strong background in machine learning, particularly deep learning and natural language processing. Experience with transformer
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for collecting and analyzing urban data (traffic, energy consumption, environment). • Strong skills in integrating IoT devices into complex digital systems. • Advanced expertise in machine learning and
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) combined with machine learning and chemometrics. Key Responsibilities: The Postdoctoral Researcher is primarily intended to support leaf spectroscopy research but will also be involved in other research
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modelling. Candidate Profile: The Center is looking for a Post-doc to work at the interface between Air Quality modeling and machine learning to evaluate air pollution in Morocco and Africa using modelling