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- Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO
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Field
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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent
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predictive modelling Development of model predictive control algorithms for energy-efficient and flexible building Contribution to prescriptive maintenance strategies and user-centric digital interfaces
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here is the prediction of the composition of communities of marine entities (zooplankton, marine snow particles, and coral reef fishes) from environmental data taken around the point of the biological
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Machine Learning models for quality prediction and control of phosphate ore. Simulating and evaluating various extraction scenarios, considering industrial constraints. Collaborating closely with field and
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systems Strong skills in data-driven analysis and modelling, simulation, control, and validation Familiar with modeling of PtX and storage technologies, model predictive control, machine learning
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(APC); model predictive control (MPC); digital twins and real-time process monitoring and control; process analytical technology (PAT); process intensification and sustainability. Where to apply Website
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well as external researchers, focusing on regional ocean climate predictions and projections. The primary objective of this position is to apply an existing Earth System Model for the Baltic Sea region (IOW-ESM) in
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Infrastructure? No Offer Description The postdoctoral researcher will contribute to the ANR-funded Pi-CANTHERM project, which aims to design, model, and predict the performance of new n‑type organic thermoelectric
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-of-the-art practical engineering models for predicting sand and particle transport struggle with the cross-shore processes (perpendicular to the beach), and they even have difficulties predicting the sign
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Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO | Grenoble, Rhone Alpes | France | 7 days ago
multi-expert segmentation databases. The postdoctoral fellow will focus on integrating segmentation variability into deep learning models, with the goal of assessing prediction reliability and enabling