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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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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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to simulate GrIS runoff, with implications for water resource management and downstream systems. This PhD project will quantify spatial and temporal patterns in subglacial water storage, and investigate
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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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in vivo genetic mouse models, advanced live and intravital imaging, engineered microchip models, primary cell co-culture systems and novel microscopy and analysis methods. The research will provide
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respond to diverse mechanical cues from their environment, including adhesion, stiffness, tension, shear, pressure, and confinement. These cues are integrated across wide spatial and temporal scales, from
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Microwave Spectroscopy system. This technology will unlock an unprecedented combination of ultra-high bandwidth and adaptable spatial resolution, allowing for the simultaneous, multiscale measurement
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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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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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relevance to extraterrestrial environments and future energy strategies. For more information on this project, see: https://copl.ethz.ch/research/research-projects/2025-saar.html Job description The PhD