* Processing and Analysis of CMIP6 climate simulations * Processing and Analysis satellite data from the European Space Agency* Decomposing climate signal into to different climate variability frequencies from daily to decadal scales* Evaluation of the representation of ESA Water Vapor and cloud cover variability in CMIP6 models at different time scales* Reconstruction of ESA signal using the best CMIP6 models at different time scales* Evaluation of the reconstruction method over the present and historical periods

* Development of an interpolation method to estimate data at local scale from ERA5 reanalysis* Monitoring of heat waves over major West African cities (Dakar, Abidjan, Bamako, Douala, Abudja, Lomé, Cotonou, ...) over the three last decades* Characterization of heat waves detected based on their intensity and persistence* Investigation of the physical processes driving heat waves over the Sahel and Guinea regions at local and large scales* Evaluation of the sensitivity of heat waves to the threshold, definition, indicators and datasets used* Evaluation of the predictability of heat waves at subseasonal time scale using ECMWF and UKMO forecast models * Investigation of the skills of machine learning models for bias correction of heat waves forecasts in subseasonal models (ECWMF, UKMO)* Implementation of machine learning models for improving heat waves forecast

* Evaluation of the predictability of Saharan heat low (SHL) in seasonal forecast models (ECMWF and Meteo France) * Assessment of the interannual variability of the SHL using wavelet analysis decomposition in time and frequency scales* Analysis the interannual distribution of SHL intensity * Analysis the intra-seasonal correlation between ECMWF forecast and reanalysis data (ERA5)* Estimation of the drift of climate forecast (ECMWF) versus reanalysis data (ERA5)* Evaluation of ECMWF and Météo-France forecasts (temperature) vs Reanalysis (ERA5) using probabilistic scores (CRPS, BRIER, ROC, RELIABITY,RANK....) * Statistical downscaling on climate forecast data* Bias correction using CDFT and QMAP* Bias estimation of forecast versus reanalysis

* Evaluation of the skills of machine learning models for climate extremes forecasting* Exploring the framework of reservoir computing (Echo State Network) for time series prediction* Simulating the evolution of dynamical and Chaotic system in meteorology (Lorenz model 1963) using Runge Kutta algorithm * Simulation of the Lorenz's system evolution using machine learning model * Analysis of the predictability of more the most sensitive points on the Lorenz attractor (center, right and left wings)

Explore the universe of Natural langage processing (NLP) and make a wonderful application on chatbot.

* Conception of a smart speech recognition engine in wolof (senegaleese local language)* Implementation of Machine Learning model based on Markov Hidden Model * Development of the Speech recognition model using the software PocketSphinx* Implementation of the Language model of the system using the framework CMUSphinx* Development of the Acoustic model using the framework CMUSphinx* Construction of the Language dictionnary (Wolof) for the language model using phonems

* Conception of Chatbot for customer service* Data processing with python* Exploring Natural Language Processing algorithms using NLTK library* Implementation of Machine learning models to support customer service in their interactions with clients

* Data mining and Processing of climate data* Statistical analysis using descriptive statistics tools* Evaluation of the relationship between atmospheric teleconnection indices (ENSO, AMO, AMM) and rainfall in the Sahel region