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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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such as landslide movement style, runout, and how landslide hazards evolve over time. This Ph.D. project will leverage the analysis of new time-series data from cloud-based satellite image archives
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extent with test beams, data analysis and development of mechanical systems. She or he will be required to interact in an international environment, meet deadlines set by the collaboration and report and
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, climate physics, geosciences or a related field; excellent skills in scientific programming and numerical / statistical analysis of simulated and observed data; a versatile mind and openness to work on a
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such as scRNA-seq, bulk RNA-seq, DNAm analysis, proteomics and clinical data integration to determine how IFN-I responses change during ageing. Application Procedure Applicants should submit a cover letter
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: The doctoral candidate will perform computational analysis of a combination of multi-omics data from the gut microbiomes of patients with Alzheimer's disease or Parkinson's disease. The candidate will use state
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-field methods) Multiscale mechanics and microstructure-property relationships Python/C++/Matlab-based simulation and data analysis Industry-facing research and technology transfer You will also benefit
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Experience analyzing and interpreting large-omic datasets. Multi-disciplinary experience in integrating cellular and molecular mechanisms and data with phenotypic, physiological, and psychological data
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field. Experimental skills in biophysics experiment design including associated data analysis. Prior knowledge of structural biology and associated data analysis techniques is of great advantage. Strong
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software) and data analysis, and a keen interest in the intersection between environmental sustainability, computing, and science. They should have an ability to communicate and present results to other