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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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% with Laura Cantini’s team and 20% with the Bioinformatics and Biostatistics HUB. Information about the teams : The Machine Learning for Integrative Genomics team : https://research.pasteur.fr/en/team
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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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, Python, Bash). Good level on machine learning. Good level of written and oral English. Ease in a multidisciplinary environment, taste for teamwork, interpersonal skills. Scientific curiosity
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.) Data analysis and statistics (R, Python) Qualified applicants should send their CV, a statement of research interests and the contact information of two referees before March 9 to david.bikard@pasteur.fr
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the main bioinformatics software and methods (R, Python, Bash). Knowledge on large-scale genotyping/sequencing data analyses. Good level in statistics. Good level of written and oral English. Ease in a
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environmental physics affects larval actions Develop robust APIs for community access Required Qualifications Essential: Strong background in finite element methods and numerical simulation Proficiency in Python
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Python-based frameworks or a web application) enabling image upload, AI-based analysis, and visualization of bacterial phenotypes. Perform image analysis using existing deep learning algorithms developed
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). Lehman, M. et al. Neural circuits underlying context-dependent competition between defensive actions in Drosophila larva. 2023.12.24.573276 Preprint at https://doi.org/10.1101/2023.12.24.573276 (2023
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consequence of the gut environmental cues on the EHEC virulence. More information and application: https://positions.stradivarious.eu/jobs/6722806-dc7-impact-of-microenvi… DC11: EHEC type IV pili structure