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methods, instruments and perform data acquisition related to the project. Write proposals, reports, scientific articles and Patent proposals related to the project. Candidate Profile Strong proficiency in
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
-dimensionality and complexity of metabolomics data, requiring advanced AI/ML techniques for robust analysis and interpretation. Integration of multi-omics data (genomics, transcriptomics, proteomics, and
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environmentally friendly alternatives. Funding information: This position is available for one year, extended to two years depending on the performance. Interested applicants should submit their online application
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attitude and ability to work both in a team and independently. Strong written and verbal communication skills, with a record of peer-reviewed publications. Proficiency with statistical analysis and data
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community: max 3-4 pages, Contact information of 3 referees. Our Offer Very competitive salary and benefits package. A unique set of research partners and collaborators. Access to a pool of highly motivated
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. without vegetation. Studying dust deposition and its impact on energy performance. Analyzing meteorological, soil, and energy production data. Contributing to the modeling of PV performance under local
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, while integrating real-time data into cloud platforms to develop reliable, secure, and scalable solutions. Main tasks and responsibilities Design, develop, and test IoT solutions for air quality
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data for model calibration and validation. Apply sensitivity analysis and optimization algorithms to refine model parameters and improve predictive accuracy. Contribute to code development, documentation
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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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metabolism. Key Responsibilities Lead metagenomic, proteomics, and metabolomic profiling of clinical and experimental samples to identify dysbiosis signatures. Apply bioinformatics tools for microbiome data