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translational model systems. The research environment provides access to state-of-the-art facilities in genome editing, biological mass spectrometry, advanced imaging, spatial transcriptomics, and 3D
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join the Tang Lab. The Tang Lab (https://tangxinlab.org/ ) develops explainable, autonomous, and multimodal artificial intelligence (AI) systems to advance biological discovery. Our research integrates
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private companies and actors from the healthcare sector. Several key infrastructure facilities are available, including specialized laboratories for spatial omics, mass spectrometry, flow cytometry, and
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Group or Departmental Website: https://plevritislab.stanford.edu/ (link is external) https://ccsb.stanford.edu/ (link is external) How to Submit Application Materials: Go to: https
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community; support multi‑omics data integration and analysis across multiple research groups; and collaborate on the development and maintenance of computational pipelines for spatially resolved
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successful candidate will work in the AI in Medicine Lab, applying machine learning to cutting-edge problems in cancer genomics, transcriptomics, spatial transcriptomics, and large-scale bioinformatics
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process, modification of the microenvironment components, and integration with state-of-the-art research technologies such as high- and super-resolution microscopy, quantitative image analysis, spatial
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or the vibrational transitions of a material, to the spatially confined electromagnetic field of an optical resonator. Most importantly, this occurs even in the dark because the coupling involves the zero-point
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Studies, MSc Urbanism and Spatial Planning). Courses will include introductory Dutch-taught courses within the BSc Geography, such as cartography, spatial data handling, and spatial analysis
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interested in candidates eager to explore the interface between cell wall mechanics and soil physical properties applying spatial gene regulation to understand root biomechanics. This position is ideally