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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
: https://www.list.lu/ How will you contribute? You will be part of LIST’s Remote sensing and natural resources modelling group Embedded in the Environmental Sensing and Modelling (ENVISION) unit
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modeling of polymeric, reinforced, and porous materials, with strong expertise in large deformations and numerical homogenization. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR7649-JULDIA
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the following research areas: Engineering – Energy Science & Engineering Engineering – Environmental Science & Engineering Knowledge of numerical modelling of thermochemical conversion processes of biomass and
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command of English. Knowledge in project field. Selection process Please see https://resurseumane.ase.ro/non-gaussian-self-similar-processes-enhanci… Website for additional job details https
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Code010374StreetPiata Roamana no 6Geofield Contact State/Province Bucharest City Bucharest Website https://resurseumane.ase.ro/non-gaussian-self-similar-processes-enhancing-mathematical-tools-and-financial-models
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-41 https://doi.org/10.1016/j.psep.2020.07.017 🎓 What you will gain • Strong expertise in advanced kinetic modeling • Hands-on experience with calorimetric instrumentation • Exposure to DFT and AI
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present. Evaluation of the trained models on suitable datasets. What you contribute Good knowledge in the field of machine learning and training neural networks. Good Python skills, preferably some
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sustainability challenges affecting the agricultural and agri-food sector, with the aim of strengthening its competitiveness and promoting processes of cooperation and knowledge sharing. Where to apply Website
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of Environmental Engineering, ETH Zürich and matriculate in ETH Zürich. The research is related to development of experimental and modeling techniques to identify emission sources, simulate the airborne transport
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Certifications/Licenses Required Knowledge, Skills, and Abilities Experience with AI-driven model updating and calibration of structural systems, including learning-based or optimization approaches Strong