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ecological modelling, spatial analysis, and interpretation of biological datasets. Demonstrated leadership of complex abalone field programs. Experience supervising HDR students, technical staff, and early
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data. High proficiency in R or comparable programming languages is required, and experience with statistical modeling, geographic information systems (GIS), and handling large spatial datasets is
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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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located at SciLifeLab in Stockholm. Our research is focused on cell biology, spatial proteiomics and machine learning for bioimage analysis. The aim is to understand how human proteins are distributed in
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, student guidance, etc. [Subject in charge] 【Master’s course】 Living Environment Design (Advanced digital spatial design) ( ): Newly planned subjects 【Bachelor's course】 Architectural Design IA
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range and optical laser pulses, combining these with near-field methods for improved spatial resolution. To better understand our results, we develop simple theoretical models. The insights gained
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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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virtual histology (spatial correlation between gene expression and cortical phenotypes). To validate findings obtained in cohort studies, we use ex vivo (e.g., brain organoids) and in vivo (e.g., mice
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their choice of preferred cults. The main methods used in the project include spatial analysis, predictive modelling, and the analysis of geocoded data, especially of epigraphic and archaeological origin. MAIN
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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