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optimization and logistics management. Data analysis and project management skills. Strong understanding of optimization concepts and profitability. In-depth knowledge of environmental sustainability standards
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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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are seeking a Postdoctoral Researcher specializing in nuclear physics and its applications, with expertise in radiochemistry applied to the phosphate industry. Key Responsibilities Analysis and Monitoring
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, or related fields, demonstrated by publications and/or fieldwork. Experience with qualitative research methods (e.g., ethnography, archival research, spatial analysis). Strong interest in interdisciplinary and
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. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic analysis, facilitating the discovery of efficient and sustainable synthetic routes for complex
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person Tentative Schedule: Year 1 1 Collection of flux data B isotopic analysis, macro- and micronutrient analysis Monitoring and analysis of flux data 2 Collection of flux data B isotopic analysis, macro
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sampling, analytical methods such as GC-MS, LC-MS, or other relevant instrumentation. Experience in olfactory system research is highly desirable. Background in experimental design, data analysis
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desalination. He/she should demonstrate experience in membrane processes, water quality analysis, and lab and pilot-scale testing of water treatment and desalination processes. Required qualifications
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analysis. Level of experience evidenced by publications in peer-reviewed journals. Fluent in French and English. Experience of working as a member of a multidisciplinary team. Ability to write publications
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advanced AI/ML methods for robust analysis and integration. Data sparsity, batch effects, and missing values across different omics layers and platforms. Cross-omics data fusion and representation learning