Standard Curriculum

Duration: 3-4 years of full-time study (6-8 semesters) 

Year 1:

• Essential knowledge of complex systems engineering: Academic load equivalent of four (4) semester-long courses (possible credit for some coursework based on prior knowledge by decision of the committee and/or requirements of prior remedial courses).
• Advanced knowledge on the subject of the research project: Minimum academic load equivalent to two (2) semester courses or tutorials.
• Engineering research workshops: Academic load equivalent of two (2) semester courses.

Year 2:

• Thesis topic approval (qualifying exam). Beginning of research thesis.

 Years 3 and 4:

• Development of research thesis: Thesis defense exam.

Download here

Core Modules

• Complexity in physics and mathematics

This module provides tools for analyzing complex phenomena in physics and mathematics, including discrete and continuous models. Examples of the former include cellular automata, agent-based simulation, neural networks, genetic networks, social discrimination networks and discrete kinetics. The category includes catastrophe theory, chaos and dynamic systems, and extended models governed by partial differential equations (fluids, transportation, kinetics). At the end of the course students will be able to simulate the behavior of a complex system using computer-assisted models.

• Statistical and computational methods

This module systematizes the statistical analysis of models of complex systems, develops inference methods for phenomena whose variables interact in complex ways and covers data mining, classification, regression and clustering algorithms that can recognize patterns and make intelligent decisions automatically based on empirical data on such systems. Management and analysis of large-scale data is also incorporated.

At the end of the course students will be able to predict the behavior of complex systems and design learning machines that facilitate automatic decision-making.

• Biological Systems

Complex biological systems are analyzed, including gene regulatory networks, metabolic networks, ecological networks and social networks, self-organization and the underlying dynamic processes. This allows students to understand relevant biological systems, apply tools and scalable engineering methods, determine key parameters of biological models and compare regulatory principles in different realms of life. At the end of the course students are expected to know how to identify common characteristics of complex systems, their emergent properties, and relate this knowledge to the development and improvement of biotechnological applications and processes.

• Decision Theory

Decision-making models are studied based on concepts from various disciplines, including cognitive psychology, economics, game theory, artificial intelligence and computer science. The emergence of decisional processes is approached based on the operation of the cognitive system. The following are covered as well: decision criteria (decision trees and deterministic domain), utility theory (lotteries, risk aversion, multiple attributes, prospect theory), game theory (dynamic games, evolutionary games) and financial options (risks, uncertainties and information). After completing the course students are expected to be able to formulate models of analysis, forecasting and decision-making applicable to management and complex systems engineering problems.

• Research and Engineering Design Workshop

The skills required to autonomously carry out original doctoral-level research projects and design solutions to complex engineering problems are developed. To do this, students engage in practical experiences designed to stimulate their creativity and innovative capacity, as well as learn about and apply advanced digital tools for the design of engineering models and products. The basic skills required to manage R & D projects, secure financing and protect intellectual property associated with academic research are also developed.

• Data Management and Analysis Workshop

The practical skills required for the management and analysis of data generated by complex systems, combining mathematical techniques, statistics and machine learning with the power of advanced computing for knowledge extraction from large-scale data («big data») are developed. It is expected that students learn database techniques and languages, visualization tools and programs to make calculations, statistical analyses or simulations. At the end of the two workshops it is expected that the student will be able to start his/her own research project leading to the PhD degree.

• Thesis

The PhD thesis reports the results of the research project developed by the student in the program. It should be an original contribution in the field of complex systems engineering.

Optional Modules:

Students can choose from a great variety of graduate-level elective courses and/or propose special tutorial courses, depending on their areas of ​​interest and desired thesis topic. These choices must be approved by the Committee. A referencial list of courses normally offered is as follows:

• Organizational Behavior, Marketing Strategy, Operations Management, Financial Management, Advanced Finance, Logistics and Distribution, Advanced Marketing,Industrial Organization, Topics in Applied Economics, Topics in Game Theory
• Molecular Biology, Biomedicine and Bioethics, Industrial Bioprocesses and Bioseparations, Environmental Biotechnology and Biometallurgy, Industrial Biotechnology, Genetics and Bioinformatics, Bioengineering and Bio-Business, Bioengineering Workshop
• Environmental Control and Quality, Applied Ecology, Energy Efficiency, Nuclear Energy, Environmental Regulation and Management, Energy Regulation and Management, Workshop on Energy and Environment, Conventional Generation Technologies, Unconventional Generation Technologies
• Systems Architectures, Management of ICT Services, Business Intelligence, Predictive Models, Networks and Telecommunications, ICT security, Mobile Telecommunications
• Optimization, Stochastic Processes, Multivariate Analysis, Advanced Econometrics, Advanced Simulation, Neuroscience and Behavior, Neuropsychology