• Advanced methods in data analysis
• Machine learning
• Statistical methods in computational intelligence
• Knowledge discovery in wide area networks
• Simulation methods
• Scientific data visualization
• Unconventional computation for real world problem solving
• Knowledge based systems and language technology
• Cooperative intelligent agents
• Declarative programming in machine learning
• Applications of computational linguistics
• Applications of computational intelligence in software engineering

• Acquisition of theoretical, applicative and practical knowledge of intelligent and computational paradigms inspired from natural science, social and linguistic fields;
• Acquisition of in-depth knowledge in modeling / design / implementation of software applications based on computational intelligence approaches for solving problems specific to natural sciences, economics, industry;
• Acquisition of the mathematical knowledge necessary in order to tackle complex problems;
• Acquisition of specialized knowledge in the analysis and improvement of software processes;
• Professional modeling for team work as well as interdisciplinary approaches to research and development.

• Advanced knowledge of theoretical, methodological, and practical developments in computer science;
• Systematic use of computer science knowledge to model and interpret new situations, within application contexts larger than the known ones;
• Detailed knowledge and integrated use of conceptual and methodological apparatus pertaining to informatics to provide solutions for incompletely defined situations, to solve new theoretical and practical problems;
• Proficient use of verification, validation, and evaluation criteria and methods to his/her own software solutions, ability to formulate value judgments and to justify/explain constructive decisions;
• Use advanced skills to develop and conduct complex software projects, of practical and/or research nature, using a wide range of quantitative and qualitative methods;
• Advanced communication skills within different professional environments, appropriate use of computer science vocabulary, good English knowledge;
• Team work abilities, assuming different execution and leading roles, performing professional tasks with considerable amounts of autonomy and responsibility.

Specialty competences:

• Demonstrate advanced modeling skills for economic, industrial, scientific phenomena and processes, by using fundamental mathematical, statistical, and computer science knowledge;
• Demonstrate advanced skills to apply intelligent methods for solving real phenomena and to develop and build procedures for efficient use of statistical methods;
• Demonstrate advanced skills to analysis, design, and construction of software systems, using a wide range of hardware / software platforms, programming languages and environments, and modeling, verification and validation tools;
• Demonstrate advanced skills to apply methods for data analysis and processing, data mining, pattern recognition;
• Ability to teach students in high schools computer science concepts and theories, provided that the holder of the dissertation diploma owns a graduation certificate of the pedagogical education module.