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): The AgroBioSciences (AgBS) program is a component of the Science & Technology pole of Mohammed VI Polytechnic University (UM6P). It constitutes a structure of higher education and practical-based research with a vision
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UM6P: At the heart of the future Green City of Benguerir, Mohammed VI Polytechnic University (UM6P), a higher education institution with an international standard, is established to serve Morocco and the
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to investigate separate valorization pathways for brines and phosphogypsum; Develop and evaluate integrated treatment strategies combining both materials, with particular attention to synergistic and symbiotic
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isolates. Conduct Bioinformatics analysis related to the generated data. Co-supervise master and doctoral student. Publish results in high impact factor journals. Education, qualifications, and experience
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environments. Job Summary The postdoc will conduct research, prepare proposals, write reports and scientific papers to study the botany and ecology of native desert plant species of south of Morocco as
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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
the Sustainable Materials Research Center (SusMat-RC) at UM6P. The successful candidate will work on an exciting project focused on extracting and analyzing experimental and computational data to develop predictive
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using in-situ XRD, XPS, 57Fe Mössbauer spectroscopy and NMR to investigate material behavior and interfacial chemistry during synthesis and battery operation. Develop mechanistic understanding of cathode
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(LLMs) to predictive maintenance challenges. Develop and fine-tune LLMs to analyze and interpret unstructured data (e.g., maintenance logs, sensor data, technical reports) for predictive insights
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of lithium iron phosphate (LFP) batteries. Key Responsibilities: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model
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: Design and implement AI/ML pipelines for multi-omics data integration, including supervised and unsupervised learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph