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Field
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in colorectal cancer. You will be responsible for developing spatially-resolved models of metastatic outgrowth in the liver which account for interactions between stromal, immune and tumour cells. You
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strategic programme. Through multiomic and spatial biology exploration of temporally distinct samples from clinical trials and advanced biological models, an international consortium of leading colorectal
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Deutsches Zentrum für Neurodegenerative Erkrankungen | Bonn, Nordrhein Westfalen | Germany | about 16 hours ago
vivo models of PD Apply and further develop methodologies such as disease modeling, virus-mediated gene delivery, behavior, immunohistochemistry, tissue clearing and 3D imaging, image analysis, spatial
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cellular dynamics. Analyze large-scale transcriptomic and spatial dynamics datasets. Work in close collaboration with the team's biologists to test predictions from statistical models. Within the Polarity
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cancer progression, immune evasion, and therapeutic resistance. We place a strong emphasis on the use of spatial biological approaches applied to human tumour models including organ/tumour perfusion, slice
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dedicated to development, translation and clinical application within medical imaging and computational modelling technologies. Our objective is to facilitate research and teaching guided by clinical
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Announcement - Mohammed VI Polytechnic University (UM6P), AgroBioSciences (AgBS) Job Title: Post-Doctoral in Modeling and Crop Yield Prediction in Africa Area of specialization: Agronomy, Modeling, biostatistics
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biologically-constrained machine learning–based model discovery pipelines to derive interpretable surrogate ODE/PDE models from simulated ABM data and spatial-omics data collected from state-of-the-art
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, and large language models (LLMs), for the analysis of high-throughput multi-omics datasets (especially single-cell and spatial omics) and large textual corpora (e.g., scientific literature). Our
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will work with data collected from the field to the spatial scale, and investigate spatial optimization approaches to improve the model parameterization at the spatial scale. We expect that you will be