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-based environments for high-performance data analysis. Knowledge of biological network inference, causal modeling, and graph-based AI approaches. Experience in multi-modal data fusion, representation
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language models for bacterial genome analysis. Train and evaluate models on large-scale bacterial genomic datasets. Apply the developed models to address specific research questions, such as gene function
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join our research team as part of an innovative project focused on the design, production, and characterization of recombinant proteins. Under the supervision of the project leader, your responsibilities
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techniques Demonstrated ability to work independently and collaboratively. Proven publication record in peer-reviewed journals. Knowledge of bioinformatics tools for transcriptomic or proteomic data analysis
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biostatistical analysis and molecular biology is highly required. The postoctorate will perform experiments and analysis both in Morocco (UM6P) and in France (CEREGE, Aix en Provence). Key Responsibilities: Design
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remote sensing datasets for environmental applications. Experience with high-performance computing and analysis of large datasets; A good track record of published research in peer-reviewed scientific
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multispectral/hyperspectral data processing. Proficiency in programming languages such as Python or R for data analysis and processing. Excellent communication skills and the ability to work effectively in a
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. Proven experience in experimental design and data analysis, with proficiency in statistical software. Experience with soil sampling, laboratory techniques, and field research. Ability to work both
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. Expertise in microbiome data analysis, metagenomics, or metaproteomics. Proficiency in bioinformatics tools for multi-omics data processing Strong publication record in relevant fields. Ability to work in a
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experience in assaying root – soil chemical interactions and imaging for simulation of soil health and nutrient acquisition Proficiency in Microsoft Office, data analysis using R and SAS. Basic knowledge