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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
disorders, and microbiome-related health issues by applying advanced AI/ML techniques for biomarker discovery and metabolic network modeling. Scientific Challenges Addressed in the Position: High
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. Strong analytical and problem-solving skills, including the ability to interpret and analyze simulation and/or experiment results. Familiarity with safety protocols and quality control measures in
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valorization is essential. Required Knowledge: Solid knowledge of acid-base equilibria, and carbonate system modeling. Good understanding of calcium carbonate behavior and dissolution kinetics. Hands
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, comminution modeling, and ore characterization, and will contribute to developing an integrated beneficiation strategy that reduces operational costs, and energy consumption. This research aligns with broader
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of Radionuclides and Heavy Metals: Develop and implement nuclear analysis methods to characterize phosphate ores and their by-products. Monitor and control the migration of radionuclides and heavy metals throughout
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on experimental and Modelling aspects. A notable characteristic of the Centre’s research is the strong collaboration among all members. We invite applications for a postdoctoral research position in the area of
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develop innovative solutions based on the analysis of urban data (big data, IoT, GIS) to monitor and improve public health. You will contribute to modeling smart cities with a focus on health and designing
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for comprehensive systems biology modeling. Identification of causal relationships and biomarker discovery through integrative approaches. Time-series and longitudinal multi-omics data analysis for disease
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efficiency, ion separation rates, energy consumption, etc. Model ion transport and system behavior under different operational conditions. Collaborate with researchers in diverse projects and contribute
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Job Description As part of our laboratory's research initiatives, we are conducting advanced research on the computational modeling and optimization of heterogeneous catalysts for various catalytic