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, Raman spectroscopy, Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), BET surface analysis, Fourier Transform Infrared Spectroscopy (FTIR), and X-ray Photoelectron Spectroscopy
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(CBS) is a component of the Mohammed VI Polytechnic University (UM6P). The main objective of CBS is to set up a distinctive research-teaching program, of international level, to meet the research and
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, concentration, recovery, or destruction of certain elements. Therefore, the chosen candidate will be expected to contribute to the performance of a wide range of scale-up studies of a magnetic and electromagnetic
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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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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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in molecular biology Excellent programming skills Experience in computational analysis of molecular data Experience with high performance computing Aptitude for team work, problem solving and
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surveys on the subject. Contribute to developing novel communication designs to optimize the WSN-IoT system/network considering communication and data fusion requirements. Conduct a theoretical analysis of
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-4 pages); 2 references letters. Qualifications and Exêrience Essential: A PhD in Organic Chemistry or related subject. Expert in retrosynthetic analysis to further elaborate novel organic molecules by
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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