In order to successfully obtain a Masters qualification, you will need to obtain a number of credits by passing individual modules. Most taught Masters will have a number of core modules which you must take and pass in order to obtain the qualification. The assessment of research Masters is almost always entirely by a single dissertation module or project.
The computing field covers a wide range of studies, including information systems, computer engineering, information technology, computer science and software engineering. Different computing disciplines may cover software and hardware system building and design, the creation of intelligent computers, information structuring and scientific research.
Romania is a country located at the intersection of Central and Southeastern Europe, bordering on the Black Sea. Getting to Romania is easy from nearly all parts of the world, due to its position, as well as the fact that it is served by an array of transport types and companies. Romanian universities offer great choice of undergraduate and postgraduate programs in Romanian, English, French and German.
Cluj-Napoca city is the capital of Cluj County in Romania and the 2nd most populated after Bucharest. It is a metropolitan area with a population of over 300,000 residents. It has around 150 pre-university educational institutions.
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The master’s program aims at providing students with the appropriate tools for further doctoral studies and professional activity. [+]
The master’s program aims at providing students with the appropriate tools for further doctoral studies and professional activity.Program objectives
Acquisition of theoretical, applicative and practical knowledge in:complex systems modeling based on mathematical concepts and methods, and on programming concepts and techniques. 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. exploitation (data-analysis, knowledge-discovering) and visualization of „big data” for computation problems, statistical interpretations, decision processes, or for scientific instruments. applicative scientific domains where high-performance systems are used. analysis and improvement of software processes. professional modeling for teamwork as well as interdisciplinary approaches to research and development. Core courses Programming Paradigms Parallel and Distributed Operating Systems Formal Modelling of Concurrency Advanced Methods in Data Analysis Functional parallel programming for big data analytics Models in parallel programming General Purpose GPU Programming Workflow Systems Resource-aware computing Data Mining Grid, Cluster and Cloud Computing Knowledge Discovery in Wide Area Networks Admission requirements ... [-]