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of novel probabilistic deep-learning models that automatically extract mechanistic and statistical knowledge from your in vivo perturbational omics data. This interdisciplinary atmosphere has been a main
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interpretation, predictive model outputs, and spatial insights. Document project needs, timelines, and deliverables while maintaining open communication with collaborators. Support proposal development and data
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translational value. Dr. Hopp’s lab utilizes state of the art models and technologies which include multiomic approaches (scRNAseq, spatial RNAseq, spectral flow cytometry, metabolomics etc.) to understand each
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Job Purpose Working under the supervision of Prof Jon Cooper, the successful candidate will contribute to a project focusing on combining spatially offset and stand-off Raman spectroscopy systems
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
Carolina at Chapel Hill. The Yates lab uses preclinical models and human tissue studies to investigate mechanisms of uterine cancer development and identify opportunities for cancer prevention
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the fall of 2025 or early 2026. The UNFoLD lab specialises in the experimental measurements, analysis, and modelling of unsteady vortex-dominated flow phenomena, with applications in bio-inspired propulsion
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model organisms in their work and are pushing into emerging model and non-model organisms that are proving uniquely valuable in particular studies. To learn more about our department, https
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), and network analyses Mathematical or statistical modeling of temporal and spatial variability to predict microbial effects in native plant and agricultural systems Development of ecological theory
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with EDF UK. The project offers the chance to develop skills in agent based modelling spatial-analysis, optimisation and economic analysis, as well as contribute to the UK’s decarbonisation. How to apply
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research team. Our work focuses on craniofacial regeneration and distraction osteogenesis, using cutting-edge multi-omic approaches (single-cell and spatial transcriptomics, genetic and biochemical analysis