• Machine Learning
• Parallel and Distributed Operating Systems
• Formal Modelling of Concurrency
• Advanced Methods in Data Analysis
• Big Data Processing
• Models in parallel programming
• General Purpose GPU Programming
• Workflow Systems
• Data streaming
• Data Mining
• Grid, Cluster and Cloud Computing
• Knowledge Discovery in Wide Area Networks

• Acquisition of theoretical knowledge in complex systems modelling based on mathematical concepts and methods, and on programming concepts and techniques.
• Acquisition of applicative and practical knowledge. in programming and usage on/of computation systems, especially those of high performance, which are necessary for solving real life problems and for simulating complex problem solutions.
• Acquisition of theoretical, applicative knowledge in exploitation (data-analysis, knowledge-discovering) and visualization of „big data” for computation problems, statistical interpretations, decision processes, or for scientific instruments.
• Acquisition of theoretical, applicative and practical knowledge in applicative scientific domains where high performance systems are used.
• Acquisition of theoretical, applicative and practical knowledge in analysis and improvement of software processes.
• Acquisition of theoretical, applicative and practical knowledge in professional modelling 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:

• Capability of developing of high performance programs based on parallel and distributed programming;
• Assimilation of mathematical concepts and formal models to understand the methods and components of high performance systems;
• Analysis, design, and implementation of data analysis systems;
• Understanding and acquisition of methods of modelling, optimization, analysis of massive datasets, data visualization;
• Demonstrate advanced skills to analysis, design, and construction of software systems, using a wide range of hardware / software platforms, programming languages and environments, and modelling, verification and validation tools;
• Demonstrate advanced skills to apply methods for data analysis and processing, data mining, pattern recognition;
• Demonstrate advanced modelling skills for economic, industrial, scientific phenomena and processes;
• 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.