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interdisciplinary team focused on developing innovative numerical algorithms and software to address emerging challenges in scientific computing and machine learning. The research will emphasize both theoretical
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Research, Variational Methods, Probability, Stochastic Processes, Complex Systems, Network Science, Linear Algebra, Data-Driven Modeling, Scientific Machine Learning, Numerical Analysis, Computational
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(especially libraries 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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skills in machine learning, deep learning, and advanced statistics for processing complex data. Urban Health Principles: Familiarity with urban planning principles centered on health (active mobility
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and machine learning to optimize treatment conditions. Contribute to the development of reproducible stress priming methods and assist in transferring knowledge to agricultural stakeholders. Required
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, Agronomy, modeling, biostatistics, or related field The applicant should have documented knowledges in Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming
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on the development of new easy, accurate, and low-cost tools for soil agricultural soils diagnosis based on the coupling of spectroscopic techniques (FTIR, NIR, Raman, …) with machine learning/ chemometrics
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devices into complex digital systems. Advanced expertise in machine learning and artificial intelligence for predictive and prescriptive urban data analysis. Experience in visualizing and analyzing spatial
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research, oncology microbiomes, or environmental resistome surveillance. Familiarity with spatial metagenomics, single-cell microbiome analysis, or multi-omics data integration. Knowledge of machine learning
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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