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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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nécessaire pour suivre les bilans des gaz à effet de serre, la production de biomasse et les rendements agricoles. À ce jour, la plupart des méthodes permettant d'estimer spatialement la GPP s'appuient soit
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are of interest. The primary objective of this PhD project is to develop adaptive statistical models for marked spatial and spatio-temporal point processes. Many real-world systems exhibit substantial spatial
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captured from UAVs. The research will address the design of AI models capable of combining heterogeneous sensor modalities, including RGB, thermal, LiDAR, acoustic arrays, GPR, and X-ray backscatter
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decision-support systems for sustainable forest-based supply chains in close collaboration with industrial partners. These projects aim to develop interactive methods, computational models, artificial
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microscopy, spatial transcriptomics and in vitro and in vivo models to study brain tumor cell-cell interactions and the organization of the cancer ecosystem. We strive for highly collaborative and inclusive
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the molecular signatures of proteostasis loss and identify early markers of proteostatic failure. The role combines wet-lab spatial biology with computational approaches. You will work across models and scales
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involving both modelers and experimentalists. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR5253-MOUBEN-002/Default.aspx Work Location(s) Number of offers available1Company
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) and induced pluripotent stem cells (iPSc) as model systems, as well as human brain tissue. In the future, and as our research program advances, we will expand our toolkit to also include mouse work and
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deep learning models (e.g., adapting methods in [6]) based on spatial cellular graphs constructed from these images to predict clinical outcomes. The research will be carried out using two