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Field
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in spatial analysis and data visualization Computer programming skills relevant for data manipulation and analysis Experience with creating and using complex data-driven analytical models using R
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a broad spectrum of research activities, utilizing state-of-the-art instrumentation at beamline 32-ID. The use of holotomography with PXM enables full-field imaging with spatial resolutions ranging
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WebGIS platforms for climate adaptation planning. Developing climate downscaling communication strategies. Fusing multiple data sources for environmental analysis. Required Qualifications PhD received
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genomic pathology approaches, including CRISPR, single cell sequencing, spatial transcriptomics, and image analysis to address biologically- and clinically-driven questions. Our overall aim is to
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cytometry Perform genomic data analyses from next generation sequencing data (eg. RNAseq, scRNAseq, spatial ‘omics profiling)—prior experience with genomic data analysis is helpful but not required Stay
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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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requirements: • PhD diploma in related field • Excellent skills in statistical analysis • Very good knowledge of English; spoken German is a benefit • Excellent scientific and writing skills Who we are: You will
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or TransCAD) or open-source transportation modeling software (i.e., MATSim and DTALite); Advanced knowledge of spatial analysis, econometrics, and/or machine learning. Additional Certifications: Additional
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ecologist (m/f/d) with a background in community ecology, macroecology, metacommunities, and/or analysis of ecological time series. The applicant should have a strong track record of scientific publications
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analyse complex data from emerging genomic pathology approaches, including CRISPR, single cell sequencing, spatial transcriptomics, and image analysis to address biologically- and clinically-driven