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Field
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optimal control theory. Proficiency in programming languages (Python, MATLAB) and experience with AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn). Experience in data-driven modeling, deep learning, and
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l'institut du thorax, INSERM, CNRS, Nantes Université | Nantes, Pays de la Loire | France | 27 days ago
Python, C/C++. • Possible co-supervision of Master’s and/or PhD’s students (on a similar topic). Where to apply E-mail Florent.Autrusseau@univ-nantes.fr Requirements Research FieldComputer scienceEducation
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, IDL or Python Familiarity with SPEDAS software 1-3 years experience in plasma physics research Must have a prior peer-reviewed publication in the space plasma physics field Application process
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programming skills (e.g., Python) Previous experience with analyzing sequencing data Excellent communication abilities, and a collaborative mindset Meritorious for the position: Demonstrated work on developing
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to offer. Qualifications: Required: PhD in ecology by start date Experience in plant phenology, biogeography, and spatial and temporal modeling (Bayesian and frequentist) Expertise in R or Python, GIS, big
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(e.g. R, Python) and an ability to work with large datasets Strong record of peer-reviewed publications Ability to independently design and execute experiments and interpret data Ability to work in a
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interpreting omic-seq data sets, with Unix/Linux operating systems, with RAID and server systems; should have good scripting/programming skills (R, Python, Bash, Next-Flow, etc.); and should have a good command
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have a Ph.D. in economics, public policy, education, or a related field by the start date, strong quantitative and econometric skills, experience coding (in Stata, Python, R, and/or SAS), and an interest
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the core Python application, ii) major contributor to the project ecosystem design and implementation, iii) coordinator of working groups, and iv) major contributor to project presentations and publications
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’ assemblies, (urban) experimentation and participatory GIS (PGIS). The selected candidate will apply a combination of these approaches to socio-spatial problems within fields such as mobility, urban studies