Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact- a multi-sensor remote sensing approach, H2020 REC projectData analysis and modelling: development of an algorithm for root zone soil moisture estimates using different types of data (satellite data, atmospheric data, land cover data) and different techniques (Machine Learning techniques, Regression, Classification), implementation of the algorithm in Python. Time series analysis, calibration of complex model parameters according to different locations and variables. In situ data validation and data fusion. Daily use of Matlab and Python programming languages. Most used Python packages: Numpy, Scipy, scikit-learn, matplotlib, Pandas.

Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact- a multi-sensor remote sensing approach, H2020 REC projectDevelopment of a Python processor to produce high resolution surface soil moisture maps using different types of satellite data. Most used Python pakages: Numpy, Scipy, scikit-learn, matplotlib, Pandas, GDAL.Data analysis and modelling: improvement of a theoretical algorithm for soil moisture estimates according to results of time series data analysis of in situ data. Use of statistical techniques like Regression and Classification. Maintenance of a Java software for satellite data processing.Programming languages and tools: Python, Git, Java, Microsoft Office, SQL, MySQL, Tableau, Bash programming, Linux, Microsoft Windows.

Processing of Biomedical Images, BrachyView project. Development of a processor for the identification of radioactive seeds in prostate cancer treatment. Use of clustering techniques, image processing techniques and Matlab software

Combination of hyperspectral and radar L-band data to study vegetation (Helmholtz alliance project)Machine learning techniques and methods (supervised and unsupervised) of statistical analysis to manage large volume of data, especially large satellite images with 368 bands and large Look Up Tables of 20 millions of simulations to get biophysical parameters from the inversion of radiative transfer models. Machine learning and statistical techniques and tools used:• Daily use of R and RStudio software• Random Forest algorithm, CARS algorithm, Support Vector Machine• Linear algebra, vector spaces, Bayesian statistics.• Classification, Regression, PCA for Dimensionality Reduction, ClusteringStrong skills in managing projects, analysing and presenting project results at conferences and meetings

SAR Focusing, SAR Interferometry,PolSAR, PolInSAR, SAR tomographyAnalysis of satellite data in complex natural systems by utilizing IDL programming language

Development of a processor for hyperspectral images processing by using techniques of Linear Algebra and Statistics and C++ programming language

Development of Web Applications with Java Technology. In particular, development of Web applications for e-commerce. Deep knowledge of RDBMS, SQL, MySQL, Oracle 9i, Toad, JSP, Javascript, HTML, Apache Struts Framework