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Centre de Mise en Forme des Matériaux (CEMEF) | Sophia Antipolis, Provence Alpes Cote d Azur | France | 8 days ago
, based on the simulated microstructures. The ATALANTE project, of which this PhD is a part, is divided between experimentation, modeling, and commercialization, and brings together five partners: CEMEF
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Job related to staff position within a Research Infrastructure? No Offer Description PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1
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sources. Implement predictive, rule-based, or optimisation-based control strategies using MATLAB/Simulink, Python, or embedded software tools. Integrate controller logic with the microgrid model and
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, such as semi-Markov models linked to disease transmission models. The framework builds on existing models but these must be adapted to account for differences in epidemiology and disease burden between
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protein or nucleic acid biophysics and structural characterization Experience with computational modeling and data analysis Excellent command of English, written and spoken Additional qualifications
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models to build customized AI pipelines for tasks such as image enhancement, detection, segmentation, and robotic control. You will contribute to both the backend development (pipeline orchestration
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-contractuel-fh-en-diagnostic-des-batterie… Requirements Research FieldChemistryEducation LevelPhD or equivalent Skills/Qualifications Skills Good knowledge of physics-based modelling and experimental skills. C1
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control framework for microfluidic live-cell analytics in close collaboration with partners at HZI, Helmholtz Munich and HHU. Your tasks in detail: Establish deep-learning–based segmentation, species
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, creating predictive models for failure control. Validation & Experimental Collaboration: Compare simulations with experiments, collaborate on proof-of-concept testing, and refine models based on results
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Intelligent Control Systems RESPONSIBILITIES Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis