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Field
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, development of data (pre-)processing pipelines, and machine learning model training to identify relevant biological states of the liver (e.g., healthy, recovering, not healthy). The (soft) sensor development
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analytics (statistical models, machine learning, uncertainty quantification) to monitor and predict cycling travel conditions from various perspectives (safety, crowding, travel time, comfort, etc
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short-term physiological responses of tree species and modified long-term dynamics of the whole ecosystem. On the other hand, vegetation demography models are numerical tools formulating forest processes
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below? Are you our future colleague? Apply now! Experience and skills · You have a strong interest in terrestrial ecosystems modelling, vegetation demography, plant physiology, and climate change
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susceptible to SM, VWC, and atmospheric delay. As a result, the objective of this PhD project is to develop models able to fuse backscattering and phase information to estimate SM and VWC more accurately. The
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a powerful way for assessing forest stress and disturbances over large areas and to monitor forest vitality over time. This research uses remote sensing technologies together with physical models and
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, ensuring strong support for every stage of the project. In addition, the institute offers access to state-of-the-art facilities and a dynamic scientific ecosystem supporting both experimental and
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of Communities team and interact with its members. The modeling work will also involve collaborations with researchers from CEFE (Montpellier), BIOGECO (Bordeaux), and forest management partners (ONF). Our little
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and what they mean for the system’s efficiency and safety. You will develop models of AI bidding strategies, analyze strategic interactions using game theory, and design optimization methods to identify
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will be to help develop a semantically integrated data ecosystem that links experimental data across our pilot plant and coatings science center using ontologies and knowledge graphs. This will require