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Field
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analysis software or programming language (R, Python, Stata). Experience in comparative analysis. Ability to integrate into a project-related work team Ability to interact with academic stakeholders in
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-based and/or learning-based motion generation for robots (particularly aerial drones). - Development: Proven experience and proficiency in C++ and Python. - Specific Knowledge: Prior knowledge of event
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field) and ground-based (radio) data - Use of various software codes: (i) radio emission simulation code, (ii) solar wind propagation code. - Development of software (preferably in Python) for data
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(interactive phylogenetic trees, Sankey diagrams). Report generation and statistical analysis • Statistical analysis of viral abundances (Wilcoxon, ANOVA), regression models (R, Python: statsmodels, scikit-learn
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bioinformatics, particularly single‑cell RNA‑seq, multi‑omics, and spatial transcriptomics analysis. Specific Requirements Proficiency in Python and R; familiarity with common bioinformatics tools and pipelines
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, granularity, semantics, attributes) using QGIS and Python for data exploration and preprocessing - designing an approach for automatic change detection using AI techniques (classification, clustering
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desirable: microwave engineering, confocal microscopy, scanning probe microscopy, magnetic resonance spectroscopy, and scientific programming in Python. Prior experience with hardware electronics such as
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analysis. - Adapt the team's existing data analysis protocols as needed. Expertise in coding (Python, ImageJ) would be a plus. - Be able to communicate research results, whether within the team
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Madagascar, and ULTIMASero, a large cohort study with collaborators in Senegal, Cameroon, and Madagascar. Required skills: • Strong programming skills (R or Python or other) • Familiarity with Git / GitHub
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» BiodiversityEducation LevelPhD or equivalent Skills/Qualifications Proficiency in the Python programming language, inferential statistics and mapping (GIS). Specific Requirements PhD in marine ecology with skills in data