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Field
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desirable. Experience with AI, machine learning, optimization, spatial analysis, scenario modeling, or decision-support methods is strongly preferred, especially when applied in transparent, interpretable
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, with particular expertise in mammary, hematopoietic and mesenchymal systems. Our research integrates human primary tissues and clinical specimens with complementary in vitro and in vivo model systems. A
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of severe disease pathogenesis caused by influenza viruses and SARS-CoV-2. This exciting opportunity allows a highly motivated postdoctoral researcher to leverage innovative experimental models, next
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Generative procedural urban modeling and rule induction Semantic enrichment and automated feature extraction Reconstruction or inference of structural detail from sparse spatial inputs Integration
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stratigraphy, weak layer formation, and avalanche initiation. Enhancing spatial and temporal snowpack datasets through field measurements, automated instrumentation, and remote sensing. Collaborate
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environments. The aim is to generate robust time-resolved data on the kinetics and spatial distribution of enamel demineralisation and remineralisation, and to support future reaction-diffusion modelling and
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for this position will join a team of computational biologists to work on multi-omic sequencing datasets (DNA, RNA, epigenetic, spatial transcriptomics and spatial proteomics) in the context of pre-clinical and
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contribute to advancing research in Earth system modeling and to improve understanding of interactions among land, water, carbon, energy, atmosphere, ecosystems, and human systems. This is a one-year
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, spatial transcriptomics, CRISPR technologies) paired with in vivo animal model systems and human patient-derived biospecimens (organoids and T cells). The candidate should have a strong background in
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learning-assisted computational pipeline for the automated detection of point defects in atomic-resolution scanning transmission electron microscopy (STEM) images. Using monolayer MoS₂ as a model system, the