51 phd-position-in-data-modeling Postdoctoral positions at MOHAMMED VI POLYTECHNIC UNIVERSITY
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Knowledge of biogeochemical cycles Experience with process-oriented modeling Selection Criteria PhD degree or Master degree in a field such environmental sciences, agronomy, applied mathematics
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data. Develop models and techniques to quantify water supply and irrigation efficiency in agricultural landscapes. Collaborate with multidisciplinary teams to integrate remote sensing data with ground
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methods for detecting the timing and frequency of irrigation events from time-series remote sensing data. Develop models and techniques to quantify water supply and irrigation efficiency in agricultural
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29 Oct 2025 Job Information Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Psychological sciences Sociology Ethics in social sciences Researcher Profile Recognised Researcher
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and application of Large Language Models (LLMs), to join our team working on predictive maintenance solutions. The ideal candidate will have recently completed (or be close to completing) a PhD in
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11 Nov 2025 Job Information Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Chemistry Engineering Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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related to staff position within a Research Infrastructure? No Offer Description Call for Postdoctoral Researchers in Artificial Intelligence (AI) – Focus on Large Language Models (LLMs) for Predictive
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candidate will work on an exciting project focused on extracting and analyzing experimental and computational data to develop predictive models for polymer-based materials. This project aims to leverage
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Duration: 12 months. Position Summary: We are seeking a highly motivated and technically skilled postdoctoral researcher to join a multidisciplinary research project dedicated to the sustainable
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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 models