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transcriptomics data and network-theoretic approaches. - design of a new mathematical method - monitoring and study of publications relevant to the field - programming/coding in Python (Pytorch) - presentation
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- python programming Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8214-SANLEV-038/Default.aspx Work Location(s) Number of offers available1Company/InstituteInstitut des Sciences
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datasets; • Strong analytical and statistical skills (preferably in R or Python); ability to analyse spatial data (with GDAL/PDAL via R or Python) ; • Solid background in fire ecology, ecosystem functioning
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datasets, and strong programming in Python and C. Familiarity with galaxy-redshift survey or 21-cm data analysis is a plus. The successful candidate will join the cosmology and astroparticle team at LAPTh
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to the field - programming/coding in Python (Pytorch) - presentation of results at conferences - interaction with team members and international collaborators Where to apply Website https://emploi.cnrs.fr
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)chemistry and expertise in MD simulations, quantum chemistry or machine learning. Knowledge of biosystems, analysis skills (Python), scripting skills (bash and/or Python) and machine learning are assets
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, library preparation, cell culture, and imaging - Proficiency in computer languages (bash, python, awk, R) - NGS/omics data analysis - Proficiency in statistics for high-throughput data analysis - Generation
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of statistical physics. Technical Skills: Proficiency in data analysis and modeling. Programming: Mastery of at least one programming language (Python, C++, Fortran, etc.). Experience: A minimum of 4 years of post
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proficiency: Matlab, Python, R, SPM, CONN; Very strong knowledge of neurophonetics, particularly stuttering and verbal disfluencies; Solid background in neuroscience and in the neuropsychology of language and
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the performance and sensitivity of a future space mission for cosmology. The candidate will be required to simulate the performance of a specific instrument using Python code in order to predict the sensitivity