Multilingual Technologies combines language and IT. This Master's degree program, which is unique in Austria, is offered in cooperation with the Center for Translation Studies at the University of Vienna and is aimed at all those who already have a Bachelor's degree in engineering or translation science and are interested in language technologies as well as multilingual solutions and concepts. The interdisciplinary character of the degree program qualifies students for future-oriented professional fields, for example in language technology or in the field of machine translation.
Master of Science
tuition fee pro semester
€ 363,361
+ ÖH premium + contribution2
Application winter semester 2023/24
1st October 2022 to 13th June 2023
30
1 Tuition fees for students from third countries € 727,- per semester
2 for additional study expenses (currently up to € 83,- depending on degree program and year)
You already have a Bachelor’s degree in engineering or in translation studies and are interested in language technologies as well as multilingual solutions and concepts. With this joint Master’s degree of FH Campus Wien and the Centre for Translation Studies at the University of Vienna, you can combine your knowledge of language and IT in a future-oriented education and training profile.
This way, fun and experience are guaranteed!
Modern laboratory equipment and high-tech research facilities enable practice-oriented teaching.
Obtain additional certificates while still studying and increase your market value.
Admission to the Master's degree program Multilingual Technologies requires a Bachelor's degree in a relevant subject (e.g. Computer Science and Digital Communications of FH Campus Wien or Transcultural Communication of the University of Vienna) as well as the following subject-specific knowledge:
Knowledge listed under a) is fulfilled by the Bachelor's degree program Transcultural Communication or the completion of the extension curriculum Language Technologies and Technical Communication at the Center for Translation Studies. Knowledge listed under b) is fulfilled by the Bachelor's degree program Computer Science and Digital Communications or the completion of the extension curriculum Computer Science (for students of the University of Vienna) at FH Campus Wien.
Regulation for the admission of third country citizens (PDF 233 KB)
Information for applicants with non-Austrian (school) certificates (PDF 145 KB)
Your application is submitted via the online application form.
You will need the following documents for your online application:
Please note:
It is not possible to save incomplete online applications. You must complete your application in one session. Your application will be valid as soon as you upload all of the required documents and certificates. In the event that some documents (e.g. references) are not available at the time you apply, you may submit these later via e-mail, mail or in person by no later than the start of the degree program.
Admission procedure: the admission procedure includes an interview with members of the admission committee (representatives of the University of Vienna and FH Campus Wien). This interview will take place online until further notice. You will receive the date for the admission procedure from the secretary's office.
This Master's degree program, which is unique in Austria and also innovative by international standards, focuses on language technologies, methods for their generation and use, and on language resources. It has a strong interdisciplinary character due to the combination of translational, transcultural, computer science and linguistic disciplines.
The joint Master's degree program will be based in the Department Engineering of FH Campus Wien and the Center for Translation Studies of the University of Vienna. It thus combines the special profile elements, professional strengths and scientific expertise of both institutions to create a future-oriented interdisciplinary education and training profile.
- Introduction to the concepts and directions of traditional linguistics
- Classical tasks of computational linguistics
- Presentation of different methods for language processing from tokenization to sentiment analysis
- Different NLP systems and computational linguistic analysis models
- Discussion of the current state of research and further research ideas
- Practical introduction to basic methods of automatic language processing
Continuous assessment
Written final examination, ongoing delivery of implementations, presentations.
Lecture, practical exercises, presentations, discussions, feedback.
English
Lector: Liad Magen
- ML definition, application areas and classification of ML algorithms (Supervised, Unsupervised, Reinforcement Learning)
- Classical ML algorithms: kNN, Decision Trees, Naïve Bayes, NN, SVM, Ensamble Learning and Random Forest
- Typical approach to ML projects: Define requirements, collect, filter and represent data, define and extract features, deploy algorithms and evaluate their performance, improve iterative ML pipeline.
- Introduction to Deep Learning: CNN, RNN, Generative Networks
Continuous assessment
Participation in discussions, elaboration of exercise examples, own ML-project, written exam
Theory transfer in class, discussion of practical examples, own ML-project
English
- Different types of language resources (terminology, lexicon, controlled vocabulary, thesaurus etc.).
- Methods for representing, creating, disseminating and using multilingual language resources, including the Linguistic Linked Open Data (LLOD) approach and linguistic Data Science in general.
- Multilingual and cross-lingual methods for improving communication using language resources and computational linguistic approaches.
- Practical examples from the field of LLOD
Final exam
Final Written Exam.
Lecture/lecture, discussion, case solutions.
English
Lector: Dipl.-Ing. Georg Mansky-Kummert, Dipl.-Ing. Branislav Miskovic
This course teaches programming concepts using the Python programming language. Knowledge of basic concepts and elemental programming experience are prerequisites. Fundamentals are repeated at the beginning of the course.
Techniques like Debugging and Tools like Git for Version control are discussed.
In addition, the following topics are discussed:
* Data structures
* Regular expressoins and search algorithms (A* algorithm, Beam search, ...)
* Usage of Application Programming Interfaces (APIs), JSON, XML
* Basics of Information Retrieval
Continuous assessment
Partial performances in the form of individual work, group work and presentations.
Oral Final Exam.
Lecture/Talk.
English
Lector: Dipl.-Ing. Georg Mansky-Kummert, Dipl.-Ing. Branislav Miskovic
This course teaches basic concepts of object-oriented programming using the Python programming language. Concepts of programming languages such as control structures, elementary data types, data structures, classes, objects and functions are taught. Furthermore, the design of programs, their analysis and techniques for debugging, tracing and testing are taught.
The course covers the following topics in particular:
- Basics of programming
- Variables and data types
- Operators
- Control structures
- Error handling
- Basics of object orientation
- Sorting algorithms
- Search algorithms
Continuous assessment
Partial performances in the form of group work and presentations.
Small group work, practical exercises, presentation of results.
English
Lector: Dr. Christian Steineder
- Probability
- Analyzing, filtering and visualizing data
- Testing hypotheses
- Statistical estimators
- Experiment Design
- Approach to statistical projects
Continuous assessment
Activity in lectures and exercises: Participation in discussions, elaboration of exercise examples, own statistical project, written exam.
Theory transfer in class, discussion of practical examples, own project
English
- Introduction to different types of translation technologies from computer assisted translation (CAT) to automated machine translation.
- Critical analysis of the perception of different technologies in the company and advantages and disadvantages of each technology
- Overview of different tools and available systems in each technology
- Overview of the current state of research and interesting open research questions in this larger topic area
- Insights into methods of quality improvement from pre- and post-editing to the revision process in translation
- Practical work with a system of computer-aided translation
Continuous assessment
Written final exam, practical exercises, presentations.
Lecture/lecture, practical exercises, discussions, feedback, case solutions.
English
- Introduction to the different approaches of machine translation from statistical to rule-based to neural and hybrid approaches.
- Introduction to basic concepts and algorithms of statistical machine translation
- Introduction to basic concepts and algorithms of neural machine translation
- Critical analysis of the advantages and disadvantages of individual systems as well as the goal and purpose of the respective approaches
- Basic knowledge of machine translation evaluation methods
- Practical introduction to concrete translation models
Continuous assessment
Written final exam, practical exercises, presentations.
Lecture/lecture, practical exercises, work assignments, discussions, feedback, case solutions.
English
Lector: Dr-.tech Dipl-,Ing Ahmad Haj Mosa
- Basics of information design
- Target group oriented design of media and information
- Design development on the basis of cognitive science principles
- Basics of Gestalt and perception psychology
- Methods of information design for different media
- Applications in web, virtual and augmented reality etc.
Continuous assessment
Partial performance through active participation in discussions and the elaboration of exercise examples, own information design project, written examination.
Teaching theory in class, interdisciplinary lecture series, discussion of practical examples; own information design project.
English
Lector: Liad Magen
- Retrieval models: boolean, vector space, probabilistic.
- Representation of content: Free text search, documentation languages, special logics, indexing, etc.).
- Machine-Learning-concepts and techniques: clustering, classification
- Deep Learning in Information Retrieval
- Web Retrieval: Link Analysis, Crawling, Search Engines
Continuous assessment
Partial performance through active participation in discussions and the elaboration of exercise examples, own IR project, written examination.
Theory transfer in class, discussion of practical examples; own IR project
English
Lector: DI Dr. techn. Mugdim Bublin
- Critical analysis of classical ML algorithms.
- Standard DL algorithms: CNN, RNN, Generative Networks
- Modern DL architectures for Natural Language Processing (NLP): Attention, Transformer, GPT, BERT etc.
- Applications of ML in general and DL in particular to NLP: text understanding, translation, speech and text generation, web search, knowledge generation
- Limitations of DL
Module exam
Theoretical lessons, discussion of practical examples, own DL-project
English
Lector: DI Dr. techn. Mugdim Bublin
- Critical analysis of classical ML algorithms.
- Standard DL algorithms: CNN, RNN, Generative Networks
- Modern DL architectures for Natural Language Processing (NLP): Attention, Transformer, GPT, BERT etc.
- Applications of ML in general and DL in particular to NLP: text understanding, translation, speech and text generation, web search, knowledge generation
- Limitations of DL
Module exam
Theoretical lessons, discussion of practical examples, own DL-project
English
- Speech Technologies and Automatic Speech Recognition (ASR)
- Fundamentals of Phonetics and Phonology
- Neural Networks for Speech Technologies
- Introduction to dialogue systems
- Practical introduction to ASR and speech-to-speech systems
Final exam
Written final examination, partial performance in the form of practical exercises.
Lecture, practical exercises, presentations, discussions, feedback.
English
- Transcultural communication from the perspective of different disciplines (with a focus on translation studies)
- communication science basics intra-, inter- and multilingual barriers and transculturality
- online collaborative translation as transcultural communication
- conceptual issues and problems
- technology assessment and ethical considerations
- transcultural communication and translation in teams
Final exam
Final Written Exam.
Lecture/lecture, discussion, case studies.
English
- Systematic research and reception of scientific works
- Correct citation
- Language register: formal vs. informal
- academic terminology and phrasing
- Structure of a paragraph
- Structure of a scientific paper
- Linguistic presentation of the chosen method, achieved results and resulting discussion
- describing statistical and qualitative data
Final exam
Writing and research exercises, written seminar paper.
Writing and research exercises, correction tasks, feedback, discussion, problem-based learning.
English
- Theoretical elaboration of different architectures in the field of neural machine translation.
- Theoretical overview of the current state of research and interesting current research topics, e.g. machine translation with only little available training data
- Critical discussion of advantages and disadvantages of the respective systems
- Analysis and discussion of current practice regarding the application of machine translation systems in companies
- Practical development of concrete current models of neural machine translation as well as their evaluation methods
Continuous assessment
Written final exam, practical exercises, presentations.
Lecture/lecture, practical exercises, work assignments, discussions, feedback, case solutions.
English
- Psychological aspects of HCI
- Usability
- User research
- Benchmarking usability
- Interaction design
- Prototyping
- Usability research and testing methods
- Usability in practice
Continuous assessment
Case studies, group exercise, written final exam.
Case studies, practical exercises, lecture
English
Organizational possibilities for structuring software development in the form of process models, such as the waterfall model, spiral model and agile models, are presented. The technical aspects of software engineering focus on the creation of object-oriented systems and their modelling in the field of machine learning.
The course covers the following topics in particular:
- requirements engineering
- use cases
- High Level Design
- Software engineering aspects in the area of machine learning
- Selected UML diagrams
- Process models
Continuous assessment
Group work, written final exam.
Blended learning, guest lectures, experiential learning, coaching
English
- Introduction to the Austrian and European legal system
- Introduction to data protection law
- Protection of privacy and general protection of personality
- Principles of processing personal data
- Roles under data protection law
- Data subject rights and obligations of the processor
- Insight into data security concepts
- Privacy by Design and Privacy by Default
- E-Privacy
- Basics of cyber security
- Tasks and powers of the data protection supervisory authority and procedural aspects
Continuous assessment
- Activities during lectures and exercises: Participation in discussions
- Participation
- Written examination
- Theory transfer in lectures
- Discussion of practical examples
English
Project management is the application of knowledge, skills, tools and techniques to project activities in order to meet project requirements. The project manager is responsible for meeting the expectations of the stakeholders in the project.
The course covers in particular the following contents:
- The immersion in the knowledge areas of project management (for example: Integration management, time management, cost management, quality management and risk management).
- Project management across cultural boundaries
- The management of virtual teams
- Legal aspects in IT projects
Continuous assessment
Written final examination, preparation of a case study.
Case studies, lecture
English
- Refresher course on research methodology
- Refresher and consolidation of good practice in scientific work
- Presentation techniques and types for scientific work
- Methods for the preparation of a master thesis concept
Continuous assessment
Oral presentation, written work in the form of an exposé.
Group work, discussion, presentation, feedback, interactive lecture with practical exercises.
English
- Presentation and discussion of the final thesis
- subject discussion
The defensio consists of the presentation and defence of the Master's thesis as well as an examination on its scientific environment and an examination covering a further examination subject from the compulsory modules which is to be substantially distinguished from the environment of the Master's thesis.
Final exam
Master exam
Independent development
English
- Independent work on a subject relevant topic based on the technical topics of the compulsory elective modules in the third semester at an academic level under the supervision of a supervisor
- Elaboration of the master thesis
Final exam
Seminar paper
Independent work supported by coaching
English
Number of teaching weeks:
18 weeks per semester
Class Schedule at the FH Campus Wien:
Fridays (full day), occasionally Saturdays (full day).
Electives
Selection and participation according to available places. There may be separate admission procedures.
As a graduate of this program, a wide range of professional fields and career opportunities are open to you. Find out where your path can lead you.
These subject-specific competences as well as the acquired interdisciplinary and methodological competences qualify graduates for careers in the scientific as well as in the private sector. Depending on the personal specialization, various professional fields open up. The interdisciplinary character of the program qualifies students for various working areas: IT sector, consulting and human resources development.
We work closely with renowned companies in commerce and industry, with universities, institutions and schools. This guarantees you contacts for employment or participation in research and development. In the course of exciting school cooperations, students may contribute to firing up pupils on topics such as our Bionics Project with the Festo company. You can find information about our cooperation activities and much more at Campusnetzwerk. It's well worth visiting the site as it may direct you to a new job or interesting event held by our cooperation partners!
Head of Degree Program Computer Science and Digital Communications, Multilingual Technologies, Software Design and Engineering
+43 1 606 68 77-2131
igor.miladinovic@fh-campuswien.ac.at
Favoritenstraße 226, B.3.05
1100 Wien
+43 1 606 68 77-2130
+43 1 606 68 77-2139
mlt@fh-campuswien.ac.at
Office hours during the semester
Monday, Wednesday and Friday from 10.00 a.m. to 12.00 p.m. and 1.00 p.m. to 4.00 p.m.
