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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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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
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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