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lung fibrosis. The ideal candidate will independently perform studies utilizing established in vitro, ex vivo and in vivo preclinical models and will have the opportunity to develop and refine novel
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. Process LiDAR data to support 3D analysis of terrain and settlements, integrating it with other spatial datasets to improve accuracy and create 3D models. Plan, execute, and process drone-based data
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Assistant Professor in Marine Biology & Ecology - Biomedical Science or Quantitative Systems Ecology
ecologist working in coastal systems, who applies modern approaches in causal inference, experimental ecology, spatial modelling, and data science, including the use of machine learning to produce rigorous
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balance modelling, the research will quantify the effect of MP contamination on melt dynamics under varying conditions. Key research questions address how MPs are incorporated into snow and ice, how
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, progression, and therapeutic response. This research is fundamental to advancing our knowledge of cancer and improving patient outcomes. See further information at the lab webpage: https://odin.mdacc.tmc.edu
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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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machine learning (ML) approaches offer a powerful framework for modeling complex catalytic materials with near ab initio accuracy while enabling simulations at significantly larger spatial and temporal
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NAME_FAMILY NAME) : https://nextcloud.univ-lille.fr/index.php/s/ezJxfSBwTjkJCnt Key words: solidification, recycled aluminum alloys, induction heating, thermal simulations, 3D modelling, mechanical testing
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. Our mission is to move beyond descriptive biology and develop predictive, mechanistic models that connect molecular regulation to cellular and systems-level phenotypes. The Laboratory of Computational
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(PDEs) and modelling with PDEs. The applicant’s research focus must be a specialisation in numerical analysis for PDEs. The specialisation should both strengthen the division’s current research in