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Your Job: In the CrowdING project, you will analyze experimental data from large crowds and develop quantitative measures to describe their spatial structure. To do this, you will use and expand
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cutting-edge research project related to the spatial transcriptome study to identify the biomarkers for cancer metastasis. The activities of the appointee will be integrated in a multidisciplinary team
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; Nguyen et al., 2023). By integrating large scale, multi-modal data and leveraging self-supervised and transfer learning, these models demonstrate satisfactory spatial-temporal simulation and predictions
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the physical and chemical processes that define the functional state of a cell. Our goal: To make 4D (space plus time) computational whole cell models of bacterial, yeast, and mammalian cells. We are working
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strategies for programming, modeling, and integrating reconfigurable/spatial architectures, such as FPGAs and ML accelerators, within heterogeneous ICT ecosystems. Reconfigurable and Spatial hardware, such as
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the framework of the ANR EmergeNS whose aim is to understand, through mathematical and computer models, the role that autocatalysis, multistability and spatial heterogeneity may have played in the emergence
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problem aims at estimating model parameters from input data, having access to a model describing how to generate the observations if the parameters to estimate were known. For instance, in optical remote
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process models from the field of spatial statistics to model clustered patterns across the landscape, and develop methods for estimating plant population size and/or change. Qualifications: Requirements
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proficiency in integrating spatial data into 3D models using sophisticated interpolation techniques. Additionally, the postdoctoral researcher will contribute to transforming mining liabilities into valuable
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analytical models. A deep understanding of environmental systems, spatial data processing, and the integration of multi-source information will be essential to ensure high-quality, science-based outcomes