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sequencing and spatial transcriptomic data analysis* Strong track record of research as evidenced by publications in referred journals* Strong written and oral communication skills as evidenced by data
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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 combining high-plex
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functional genomics, supported by extramural funding. Develop novel technologies and/or apply advanced spatial analysis to study tissue microenvironments in health and disease. Engage in undergraduate
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leading role at the international level, for example in the field of plant and microbial data management, in the evaluation of new methods of genome analysis, in the integration, interpretation, and
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etc) Success in research funding and/or experience in grant development and writing. A good understanding of appropriate statistical analysis techniques. Qualifications Mandatory: PhD in Biology
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to complementary expertise in energy systems, spatial analysis, and artificial intelligence. The PhD position will be formally hosted at Empa, with opportunities for research stays at partner institutions in
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to the Dillon Lab’s multidisciplinary research program evaluating the sustainability of animal-source foods. The position combines spatial analysis, livestock systems modeling, and environmental assessment
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methods for forensics, life sciences, topological data analysis, spatial statistics, and computational statistics). The department values an informal atmosphere with strong collegial support, openness, and
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methods for single-cell data analysis (tools developed by the team : https://github.com/cantinilab ). Single-cell high-throughput sequencing, extracting huge amounts molecular data from a cell, is creating
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methods for single-cell data analysis (tools developed by the team : https://github.com/cantinilab ). Single-cell high-throughput sequencing, extracting huge amounts molecular data from a cell, is creating