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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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activities. Qualifications: Ph.D. in Bioinformatics, Computational Biology, Computer Science, Genomics, or a related field. Strong background in machine learning, particularly deep learning and natural
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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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internationally oriented institution of higher learning, that is committed to an educational system based on the highest standards of teaching and research in fields related to the sustainable economic development
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embedded software for CubeSats. They will teach embedded systems and spacecraft engineering and lead experimental research on robust satellite architectures and mission reliability. Key qualifications: PhD
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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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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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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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, 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