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Doctoral Program in Informatics Engineering

General information

Official Code: 5141
Acronym: PRODEI

Informatics Engineering is seen as a broad Body of Knowledge encompassing several aspects of Computing Engineering, Information Systems and Computer Science contributing to the Conceptualization, Specification, Implementation, Validation, Maintenance and Integration of Computer-based Systems.

The main objective of this Doctoral Program in Informatics Engineering (ProDEI) is to promote excellence in the Applied Research in Informatics, including theoretical aspects behind modeling, design and implementation phases of Computer-based Systems life-cycle.


  • Doctorate in Informatics Engineering (180 ECTS credits)
  • Advanced Studies (3rd cycle) in Informatics Engineering (60 ECTS credits)

Courses Units

Service Oriented Architecture and Web Services Interoperability


Information Systems Integration

PRODEI005 - ECTS Provide the students with the conceptual and reference models to tackle in a systematic and compreheensive way a project of intra- / inter-enterprise integration.
This course also lays the basis for starting research in the area of Enterprise Integration.

Interaction and Visual Simulation Environments


Provide the students with advanced knowledge in interaction techniques, namely in environments of virtual and augmented reality.

Methodologies for Scientific Research


- To make young Researchers acquainted with the Methods, Processes and best Practices associated to Scientific Research in several different scientific domains as well as in Informatics (Computer Science and Computing Engineering). - To develop scientific critical attitude. - To enable the students to adequately produce scientific papers and thesis according to all the relevant steps that are expected for this kind of texts. - To convey to the students all the needed processes related with the organization, participation and monitoring of scientific meetings. 

Advanced Methods of Modeling and Simulation

PRODEI026 - ECTS The aims of this course unit may be considered in two perspectives. First of all, this course unit aims to present the area of modelling and simulation as an important resource and indispensible tool in the scientific method in order to test and validate concepts and theories, which are useful in almost all PhD projects. Subsequently, the presentation and discussion of recent techniques and methods of modelling and simulation should motivate the identification of problems and challenges, which make this area an excellent study subject in the domain of computer engineering.

In more detail, the aims of this course unit are:
- to present the basic concepts of (computational) modelling and simulation and their diverse areas of application;
- to present the different phases of the life cycle of a simulation project, from the modelling phase to the analysis and application of results;
- to present the main types of models and their adequacy to the treatment of different problems, as well as the main simulation techniques.
- to extensively present and discuss the state of the art of recent technology in the area of modelling and simulation, by identifying the challenges, main areas of investigation and trends.

Language Processing and Information Extraction


The main objective of this course is to equip students with knowledge about natural language processing and information extraction techniques, combining the presentation of theoretical foudations with pratical applications.

Constraint Programming for Combinatory Optimization


The investment made by companies / institutions in the development of information systems to support their operations allows them to collect more and better data on these operations. This information enables a better understanding of how the organization works and creates opportunities to optimize its processes. This Course focuses on an approach for solving optimization problems, constraint programming.

Thus, the main objectives of this Course (UC) are:

  • Motivate for the use of constraint programming techniques for solving complex optimization problems
  • Develop the ability to properly utilize these techniques and the tools that implement them to solve real problems.

Percentage distribution:

  • Scientific component: 70%
  • Technological component: 30%


Intelligent Robotics



    • To understand the basic concepts of Robotics and the context of Artificial Intelligence in Robotics.


    • To study methods of perception and sensorial interpretation (emphasizing computer vision), which allow to create precise world estates and mobile robots’ control methods.


    • To study the methods which allow mobile robots to navigate in familiar or unfamiliar environments using Planning and Navigation algorithms.


    • To study the fundamentals of cooperative robotics and robots' teams construction.


    • To analyze the main national and international robotic competitions, the more realistic robot simulators and the more advanced robotic platforms available in the market.


    • Improve the ability to communicate regarding scientific and technical issues.


    • Improve healthy scientific approach.


Multi-agent Systems


Global perspective about the techniques assotiated to the specification and design of computational software agents and Multi-agent systems (MAS). To understand the practical importance of MAS applications.

The main Goals involve: the recognition of when to use this distributed and decentralised approach, as well as to know how to do it; To specify models of agents' architectures and multi-Agent Systems interaction.

To look at Agent oriented programming paradigm as a new metaphor for designing distributed computer systems. To know how to formalize through intentional logics agents knowledge and functionalities.

Test and Quality of Software


Be familiarized with the terminology used in software testing. Acquire knowledge about the main techniques that may be applied in software quality control. Acquiring sensitivity to issues relating to software quality processes.

Advanced Topics on Knowledge Extration and Machine Learning



It is current practice of Corporations and Research Institutions to collect and store huge amounts of data.
The analysis of such  data can become a competitive advantage (for business) or as a source for new discoveries (in research).
Analysing large amounts of data or complex data manual processes, or even OLAP, are prohibitive.
Computational tools, using data analysis algorithms (from Statistics, Machine Learning, Data Mining, etc.), are necessary for the [semi]-automatic construction of models that help decision makers and researchers to solve data-based complex problems.


Motivate the students for the use of Data Mining techniques as decision support tools. Develop student's skills to correctly use DM techniques in the analysis of very large data sets. Make the students aware of advanced DM topics.

Advanced Topics in Informatics Engineering


According to the Regulations applied to PRODEI Program, this Course is available for those students who need to learn about subjects that are not made available by existent courses included either in this or other current Programs at the University of Porto. Each student attending a "Free Study" course has to deliver a final report on the work done under the supervision of a specific, previously assigned Instructor, who also is responsible for the evaluation. It is up to the Scientific Committee of PRODEI the responsibility of selecting and assigning the appropriate instructor and agree with the specific subject for the course.

Software Engineering Research Topics

PRODEI014 - ECTS To present research topics and recent improvements in the field of Software Engineering, in order to prepare students to conduct a doctorate research work in the field of Software Engineering.

Analysis of Social and Information Networks


Social media services have led to the emergence of huge amounts of user-generated content in the World Wide Web, which are valuable sources of information and business intelligence. Underlying these services are rich social structures comprising millions of individuals and organizations, who interact online every day through information and media sharing. Analysis of such social structures is fundamental for understanding users behaviour and network dynamics. The first part of the course covers the fundamentals in graph theory, social network analysis and visualization. The second part addresses structural and dynamical properties observed in large scale networks. The final part of the course presents several network mining applications.

High-Performance Embedded Computing


O1. To learn about the processes, methodologies, and best practices associated to the development of applications in the context of high-performance embedded computing systems;

O2. To develop a scientific criticism spirit and skills for analysis of scientific work in the high-performance embedded computing research area;

O3. To acquire the capability to conclude and present a project related with the development of an application in the context of high-performance embedded computing systems;

Planning and Scheduling Methodologies


To address planning and scheduling problems in an integrated perspective.

To study traditional approaches to planning and scheduling problems.

To explore recent planning and scheduling methodologies, based on heuristic algorithms from the domain of Artificial Intelligence.

To apply heuristic techniques for planning and scheduling in problems of medium complexity.

Programming Paradigms



In previous years of the course the students learn several programming languages. Nevertheless,they missi a global view of Programming Languages.


Provide teh students with a global view of the different programming paradigms enphasising the concepts, implementation and adequacy to the class of problems so they can understand the trade-offs in the design of programming languages.

Research Planning


To enable PhD Students to discuss and select their own thesis subject. To help students to explain their decision and write the State of the Art Report on thye respective scientific subject as well as their Thesis temporal Plan.

Assisted Reconstruction of 3D Objects


Give the students a deep knowledge about 3D Solid Modelling, namely in its use in the development of 3D Reconstruction systems.

Information Retrieval



Information retrieval (IR) deals with automatic methods for computing answers to queries on large document collections. Answers may take different forms, from document lists to document summaries, from XML elements to entities in documents. For some information retrieval tasks there are currently well-known tools used for accessing online information; Web search engines are a standard example.

Specific objectives:

  -Motivate students to the variety os tasks in information retrieval and to their multiple uses;

-Make the students familiar with the main concepts in IR and with the proposed models and techniques;

-Explore existing tools for building information retrieval systems and apply them to datasets from a selected domain.


High Performance Resources in Internet Environment


The main objectives are to provide the students with a solid knowledge of parallel computing (in clusters) and distributed computing in the Internet infrastructure, mainly Grid Computing and Cloud Computing. Also we can enlist as objectives the obtention of a solid knowledge on parallel architectures and on developing parallel programs for distributed memory and shared memory architectures. Through assignments and lab projects the students will also obtain experience in the core technologies in the field, including performance analysis and tuning.

Social Simulation and Complex Analysis Systems


This course is intended to present and study Complex Systems from a behavioural perspective, where macro-level consequences result from micro-level interactions of entities networking in social phenomena such as co-operation, collaboration, competition, diffusion, foraging and complex societies. The proposed programme aims at presenting all concepts and tools for the practical implementation of social simulations with a diverse range of applications in mind. More specifically, the goals are:

  • To introduce and discuss on concepts and basic characteristics of social systems as a metaphor to analyse complex domains;
  • To present and practice with modelling techniques and simulation tools to analyse complex social systems;
  • To present and define all steps in the life-cycle of simulation project applied to the analysis of complex social systems;
  • To present techniques to test, verify and validate social simulation models;
  • To present appropriate tools to simulate social systems;
  • To carry out a complete social simulation project. 

Electronic Business Technologies


Conducting business electronically is an increasingly ubiquitous approach. This course addresses this concept from a scientific-technological point of view.

Specific aims are: To understand the importance and opportunities of Electronic Business; To explain Electronic Business Models; To present Support Technologies for Electronic Business; To explore the application of multi-agent systems to model electronic business scenarios.

Scientific component:50% Technological component:50%

Fault Tolerance in Computing Systems


This course unit aims to trigger research works on the topic of Safety-Critical Computing Systems This course unit will cover notions related to safety, which are fundamental during the development life-cycle of Critical Systems. The main topics covered in this course include: a) the life-cycle phases for the development of safety critical systems, since its design and specification, to its certification, installation, maintenance and deactivation; b) Information about how to assess safety and how to determine the required measures to develop safety-critical computer systems; c) the available techniques to deal with critical safety aspects in computer systems and methodologies that can be used to improve its reliability.

Advanced Topics in Informatics Engineering


According to the Regulations applied to PRODEI Program, this Course is available for those students that need to learn about subjects that are not made available by existent courses belonging to this or other current Programs at the University of Porto. Each student attending a "Free Study" course has to deliver a final report on the work done under the supervision of a specific, previously assigned Instructor, who also is responsible for the evaluation. It is up to the Scientific Committee of PRODEI the responsibility of selecting and assigning the appropriate instructor and agree with the specific subject for the course.

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