The goal is to transform the results of a long standing collaboration between the Inria Statify team and the Institut de Planétologie et d’Astrophysique de Grenoble (IPAG) into state-of-the-art software tools for the research communities of computational statisticians and astrophysicists.The job consists of improving the quality of the existing PlanetGLLiM software, updating its inverse problem solver to the latest state-of-the-art, and publish it. Besides, we aim at adapting the PlanetGLLiM software so that it can run on a distributed computing infrastructure. This effort should accelerate research on statistical inverse problem solving and will allow planetary scientists to efficiently analyse the increasing amounts of available hyper-spectral imagery products.Development of statistical inverse problem solver library called "xllim" (https://gitlab.inria.fr/kernelo-mistis/kernelo-gllim-is).This library is a toolbox based on the GLLiM method - a probabilistic model with very interesting application in inverse regression. I have reworked and restructured the whole C++ library and I also contributed to develop more performing and optimized methods. The library is deploy through a Docker image. There is also a Jupyter notebook with xllim Docker image available. Framework: C++, Armadillo, pybind11, Docker, cmake - Gaussian Mixture - Bayesian analysis and modeling - Vectorization of methods - C++ implementation of new physical models and statistical methods - Compilation with CMake - Documentation host on Gitlab Pages - Performance test and analysis - Gitlab CIDevelopment of astrophysic software based on Kernelo library called "PlanetGLLiM" (https://gitlab.inria.fr/kernelo-mistis/planet-gllim-front-end/)Framework: Python, Django, Vuejs, Bootstrap, Docker Compose - Rework of the app - New user interface - Documentation host on Gitlab Pages - Gitlab CI

The mission consists in a complete approach to greenhouse gas modeling, scientifically and economically relevant for application purposes.- Scientific literature review- State of the art of complex dynamic systems modeling, the rebound effect and interaction systems between population, environment and ecosystem- Development of mathematical models with several methods- Get useful information (demand, production, carbon price...) from worldwide economy databases- Implementation (Python)- Numerical tests and model evaluation- Writing of a scientific research note

Development of an online platform to manage orders for the e-commerce marketplace. Programming of the functionalities with Ruby on Rails (Back-end), design of the graphical interface for international sellers with VueJS (Front-end) and API integrations.I started with a 6 month internship and then continued for 5 months part-time during my last year of study.- Ruby (Rails), Javascript (VueJS et NodeJS)- Communication in foreign languages (English and Portuguese)- Understanding of the Brazilian and global e-commerce market

Programming Python functions covering thermodynamic and management of the HRS. Tests, analysis and optimization of the simulator. Programming in VBA and JavaScript to link with Excel and Google Sheets interfaces.- Python, Excel-VBA, Javascript- Thermodynamics- Results comparison- Code improvement