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, including but not limited to algorithms, databases, cloud computing, machine learning, operating systems and security. Jobs Summary: UM6P invites applications for post-doc, in all areas of Computer Systems. A
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concepts and profitability. In-depth knowledge of environmental sustainability standards. Experience with data prediction and classification techniques. Computer Skills Good proficiency with optimization
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. The main objective of the project is to study the interaction between machine learning and wireless communication fields. The successful candidate will answer questions such as how to assign limited
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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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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
, and Precision Health. The project aims to leverage AI and machine learning (ML) to analyze complex metabolomics datasets and address key health challenges, including biomarker discovery, disease
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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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. Experience with data prediction and classification techniques. Computer Skills: Good proficiency with optimization tools (CPLEX, SAP). Experience with data analysis software. Soft Skills: Analytical mindset
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Osmosis (FO) — Reverse Osmosis (RO) system. The work of this project includes lab work, computer modelling, life cycle assessment, and techno-economic study. The project will contribute to protecting water
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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