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Q: EM virtual prototyping

Thesis Code: 20014 Thesis Type : Master Thesis for Telecommunication/Electronic Engineering, Computer Science, Mathematics, Physics or equivalent Research Area : Advanced Computing and Applications Requirements MS students in Telecommunication Engineering, Electronic Engineering, Computer Science, Mathematics or equivalent Experience with main programming languages (Matlab /Fortran/C/C++) Basic knowledge of EM fields Good knowledge of linear algebra and linear systems Description Solutions to Maxwell’s equations are known only for a few simple geometries; this is where the scientific discipline known as Computational Electromagnetics (CEM) comes into play, aiming at a numerical solution of the equation in presence of non-trivial geometries/materials. The thesis aims at developing fast and efficient algorithms for the solution of Maxwell’s equations, with special attention to: large patch antenna arrays large and complex structures (e.g., satellites, aircrafts, etc.) Contact : send a resume with attached the list of exams to marco.righero@linksfoundation.com specifying the thesis code and title.

Q: Monitoring Plants Phenology with Machine Learning Techinques

Thesis Code : 20011 Thesis Type : M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent Research Area : Data Science for Industrial and Societal Application Requirements : Knowledge of Python Software development skills Basic concepts on data science, concerning data analysis, processing and machine learning Basic concepts on image processing Description : Monitoring phenology of agricultural plants is a critical understanding in precision agriculture. Vital improvements can be achieved with precise detection of phenological change of plants which would henceforth improve the timing for the harvest, pest control, yield prediction, farm monitoring, disaster warning etc. Many countries across the world have been developing initiatives to build national agriculture monitoring network systems, since inferring the phenological information contributes to a better understanding of relationships between productivity, vegetation health and environmental conditions. The objective of this thesis consists in the study and implementation of machine learning algorithms useful for plants phenology monitoring. The proposed algorithms will be trained using open datasets and proprietary datasets. The candidate will have both the task of creating the dataset and studying and evaluating the best algorithms to apply for the case of study. The candidate is required to implement machine learning algorithms, with the exploratory possibility of deep learning algorithms using popular framework (TensorFlow, PyTorch, Keras, etc..). Contact : send a resume with attached the list of exams to mirko.zaffaroni@linksfoundation.com specifying the thesis code and title.