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English Language Master's Programme Computer Science

Never before in history has there been such a rapid growth in knowledge as in areas of information technology. By taking part in the English-language master's degree programme Computer Science, you can successfully help shape some of these areas of rapid growth, such as information security and robotics, machine learning and artificial intelligence.

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Quick Facts

  • Duration of study: 4 semesters
  • ECTS credit points: 120
  • Academic degree: Diplom-Ingenieurin or Diplom-Ingenieur (Dipl.Ing. or DI), equivalent to the Master of Science (MSc)
  • Language of instruction: English

The Master's Programme

In the master's degree programme Computer Science, you expand and deepen your fundamental knowledge in the field, as well as knowledge about systematic and automated information processing technologies and applications. You learn to use both formal mathematical and engineering methods and to integrate findings from natural science and technology into computer science. In this way, specific application problems can drive further development in basic computer science.

You have the opportunity to participate in research and industrial projects during the course of your studies. You can tackle tasks such as the use of drones to deliver parcels, the development of games (games engineering), or the detection chip errors. You make contributions in the form of the conception, programming, or development of algorithms.

The master's degree programme Computer Science differs from other degree programmes in the IT sector in that it is a fundamental education programme that has strong methodological and algorithmic components.

Students have access to excellent research infrastructure, including

Curriculum (in German)

Semester plan

Focus Areas

Students have a great deal of freedom when choosing court content. The master's degree programme Computer Science consists of a major subject (major), a minor subject (minor), a free-choice subject and a master’s thesis.

You can specialise still further in the following focal areas:

Algorithms and Theoretical Computer Science (as a major or minor): You learn the fundamentals of probability and information theory. In addition, you become familiar with the most important problems and algorithmic approaches used in combinatorial optimisation to solve these and acquire in-depth knowledge of discrete geometric structures.

Data Science (as a major or minor): You learn how to perform data mining and become familiar with the architecture of modern machine learning systems. For example, you perform statistical analyses on large amounts of data.

Games Engineering (as a major or minor): You acquire both fundamental and more detailed knowledge about game development, game design, real-time graphics and simulation and animation techniques. You can develop an initial game prototype.

Information Security (as a major or minor): You learn more about the challenge inherent in making the digital world more secure and, for example, how to create secure systems such as ATM cards or chips. You focus on practical aspects related to applying and using security mechanisms and understand the principles underlying these mechanisms. Further information about Information Security.

Intelligent Systems (as a major or minor): You deepen your knowledge in the field of artificial intelligence and learn more about knowledge representation and inference methods. Furthermore, you familiarise yourself with natural language processing and intelligent user interfaces. You learn how to identify the correct approach to use to solve problems and to design and apply user interfaces.

Interactive and Visual Information Systems (as a major or minor): You explore the design, development and evaluation of user-based information systems. You also learn, for example, technologies that can be used for data representation, navigation and presentation. In addition, you familiarise yourself with web technologies and evaluation methods.

Machine Learning (as a major or minor): You learn fundamental mathematical skills and how to use machine and autonomous learning. You also take a closer look at methods used for deep learning and reinforcement learning.

Robotics (as a major or minor): You learn which methods can be used to develop intelligent robots and systems. In addition to acquiring basic theoretical knowledge about navigation, computer vision, machine learning, knowledge representation, decision-making, or speech comprehension, you also acquire the ability to design, apply and validate intelligent systems.

Software Technology (as a major or minor): You explore advanced techniques that are relevant to the development of complex and critical software. These include analysis, design, validation and verification techniques. Furthermore, you learn how to apply artificial intelligence techniques to software engineering. You also become familiar with programming languages and compiler construction.

Visual Computing (as a major or minor): You learn about computer graphics, image processing, geometric modelling, virtual and augmented reality and information visualisation. In addition to mastering the theoretical basics in these areas, you will learn how to apply these in practice. The areas of application range from medicine to industrial automation.

Supplementary Mathematical Foundations (minor): Numerical approximation methods are essential for the simulation of technical processes. You learn the fundamentals of numerical mathematics and focus on how to describe and understand the algorithms.

Supplementary Statistics (minor): You learn how to use methods to model and analyse dependent variables. You learn the fundamentals of multivariate statistics as well as methods used in dimensional reduction, classification and experimental design.

Supplementary Embedded and Mobile Systems (minor): You learn more about the architecture of embedded systems and their hardware and software, such as microcontrollers and basic software. Furthermore, you become familiar with the associated software development and design models.

Collaborations and Networks

Students benefit from accessing the teaching staff’s research network and can conduct thesis work at top universities, such as RWTH Aachen, ETH Zurich, or Stanford University. In addition, visiting international professors regularly provide insights into their research.

It is possible to conduct research and industrial projects with regional cooperation partners such as AVL, Magna or Infineon, providing insights into practical applications. Support is also provided for master's thesis work in cooperation with local industry partners and for stays abroad. Students even have the opportunity to follow up on their master’s thesis by performing specialised dissertation research.

Double-Degree Programme with the University of Ljubljana – Artificial Intelligence/Machine Learning

TU Graz and the University of Ljubljana have teamed up to offer a specialised double-degree programme in Artificial Intelligence and Machine Learning. In this programme, you study at least one semester at the University of Ljubljana and complete at least 30 ECTS credit points. Your master’s thesis will be supervised by teaching staff at the TU Graz and teaching staff at the University of Ljubljana.

For further information, please contact the Dean of Studies.

Admission

The prerequisite for admission is a completed bachelor’s degree in a relevant subject.

Additionally, you need to provide evidence of competence in the English language.

1. Admission Procedure

To enter the master’s programme, you have to undergo an admissions procedure.

Registration for the academic year 2021/22:
15 October to 15 December 2020

You can complete the admissions procedure before the end of your bachelor’s studies.

To the admissions procedure


The following categories of applicants are eligible for the master’s programme without an admissions procedure:

1. Graduates of the following bachelor’s programmes at TU Graz:

2. Graduates of the following Bachelor’s programmes at other universities:

Graduates of all other degree programmes offered at the TU Graz can apply for admission at the Registration Office without an admission procedure.

2. Admission

If you have already been admitted to a degree programme at TU Graz, or have studied at TU Graz before, please come to the registration office in person to complete your admission during the admission period.

Information and Advice

Contact studynoSpam@tugraz.at

Career Prospects

Florian Geigl
Florian Geigl, graduate; CIO/CDO at the IBS Paper Performance Group

As a graduate in the field of computer science, a whole world of opportunities is open to you. You can apply for positions in almost any branch of industry. In the end, you can choose between multiple job offers. After graduation, I started working as a data scientist. At present, as CIO & CDO at the IBS Paper Performance Group, I'm responsible for the company's information technology and its digital transformation.

Professional Fields

Graduates of the degree programme have both abstract and model-oriented thinking skills. They can successfully apply the knowledge and methodical approaches they have learned in industry, business and science:

  • You design, operate and assess complex hardware and software systems in the information technology field, the automotive industry and in telecommunications.
  • You conduct research at universities, other research institutions and industrial research and development departments.
  • The systems you design are used, e.g. in smartphones and apps or in robotic systems.
  • You work nationally and internationally, independently, or as an employee, e.g. in industry, young start-up companies, or in the service sector.