This course is divided into four semesters. The first semester is intended for the transmission of a body of basic knowledge and the transmission of the very process of knowledge construction, comprehension of the scientific method, and transmission of the big basics and how they integrate. The course aims to establish afresh a variety of important concepts namely in the disciplines of optimization and algorithms, statistics, probability, and stochastic processes, adaptive structures - dynamics and control and distributed systems and networking. These four courses are not optional.
The second semester will provide the foundations of Information Engineering, covering advanced topics in statistical signal processing, information theory, and computational learning. In addition to these three non-optional courses, students will be able to choose one complementary course from a set including network analysis, cloud computing, and security and trust.
The third semester will allow students to understand the analysis and use of Information in diverse Engineering Systems and includes courses on smart grids, intelligent transportation systems, bio-image understanding, network management, and finance. Students will choose 3 from the set of available courses in this semester. We expect the topic of the thesis to be related to one of these courses.
The dissertation will start in this semester, mostly with work on the state of the art. The fourth semester will be spent exclusively on the thesis research, development and writing.
* MEINF will establish strong connections with International Programs (MIT|Portugal Program, CMU|Portugal Program, Austin|Portugal Program), with joint classes, PhD students and research projects.
MEINF aims at offering advanced formation in information technology and systems with emphasis on the generation, distribution, analysis and use of information in engineering systems.
MEINF educates engineers to be able to design, manage, build and test engineering systems centered in information and knowledge processing. Students will learn to design, analyze and implement algorithms based on mathematical models to be applied to complex systems of the industrial or corporate world, and to create efficient strategies to optimize their performance. Throughout their training, students will acquire both the theoretical and methodological tools which will be applied in all fields of engineering and other domains including economics, environmental sciences, and life sciences.
MEINF targets both national and international audiences and is designed for excellence and competitiveness at the international level. It aims to prepare selected students for leadership in research and development careers in industry, academia and independent entrepreneurial initiatives.
Some scholarships covering part of the tuition and living stipend are available for the second year and will be awarded based on merit.