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Field
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analysis of high dimensional datasets derived from clinically annotated gynecologic cancers including spatial transcriptomics. Proven ability to write successful grants and lead projects is expected. A
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populations within the TME are relevant to tumour evolution and patient survival outcomes [2-4]. This PhD project will seek to investigate the prognostic value of spatial immunophenotypes in lung cancer
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with the applicant’s qualifications and experience. Department: https://msi.ucsb.edu/ Qualifications Basic qualifications (required at time of application) Applicants must have completed all requirements
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developing approaches to leverage spatial data to better understand evolutionary histories. More information about the lab and their work can be found by visiting https://federlab.github.io/ About the
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lead an independent research program in computational oncology, centred on new quantitative and machine-learning methods that integrate single-cell genomics and spatial profiling to study cancer
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written communication skills in English English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations
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responsible for the development and implementation of data analysis methods to be used on two-photo imaging datasets collected by scientists within the center as well as the development of experimental
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the role of membrane dynamics and lipid environments in modulating signaling scaffolds. We investigate how membrane-remodeling complexes and specific lipid compositions influence the spatial organization
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Analysis for Planning Renewable Energy (MapRE) initiative, a spatial analysis platform for planning renewable energy deployment, and GridPath, an electricity system planning and operations analysis platform
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spatial and temporal data analysis using advanced machine learning technologies. The successful candidate will become a part of an interdisciplinary team working to develop machine learning techniques