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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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by retinal damage alone. Instead it is likely that microstructural damage in the brain as well as the eye contribute. Since early risk prediction and timely intervention can mitigate vision loss, it is
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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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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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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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(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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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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and protocols to produce, downscale, assess and deliver state-of-the-art decadal predictions and multi-decadal projections of climate change and related impacts on marine ecosystems, covering the basin
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execution time (WCET). The postdoc position focuses on compiler support for WCET analysis for time-predictable architectures such as Patmos/T-CREST. Furthermore, it is expected to join the development of a
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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