CU Carinthia University of Applied Sciences

M.Sc. in Medical Engineering and Analytics

The Master’s degree program in Medical Engineering & Analytics at CU Carinthia University of Applied Sciences is an English-taught, practice-oriented program that combines engineering, artificial intelligence, data analytics, and healthcare. The program is designed for students who want to develop innovative technological solutions for diagnostics, therapy, healthcare, and digital health. By combining medical engineering with modern data-driven technologies, the program prepares students for a rapidly developing field in which artificial intelligence and digitalization are changing the way healthcare is delivered.

The program comprises 120 ECTS credits and takes four semesters to complete. Graduates receive a Master of Science in Engineering (MSc). The language of instruction is English, with a minimum recommended level of B2. The program is offered at the CU campus in Klagenfurt, Austria. One of its most distinctive features is its flexible HyFlex learning model, which allows students to participate on campus, online, or partly asynchronously. This structure makes it possible to combine the Master’s degree with professional employment and personal commitments.

The Importance of Medical Engineering and Digital Healthcare

Healthcare is becoming increasingly dependent on advanced technologies and large amounts of data. Hospitals, medical devices, diagnostic systems, and healthcare institutions generate enormous quantities of information, including medical images, physiological signals, electronic health records, and other forms of patient-related data.

The challenge is not simply to collect this information but to use it effectively. Artificial intelligence, machine learning, deep learning, and data analytics can help healthcare professionals identify patterns, support diagnoses, improve treatment decisions, and develop more personalized forms of healthcare.

Medical Engineering & Analytics focuses on this connection between technology and healthcare. Students learn how engineering methods and data-driven technologies can be used to address real problems in medicine. The program therefore combines technical expertise with an understanding of medical applications and the requirements of the healthcare sector.

The goal is to enable graduates to develop intelligent solutions that can contribute to the healthcare of the future. These solutions can include systems for medical imaging, physiological signal analysis, clinical decision support, assistive technologies, and digital health applications.

Main Areas of Study

One of the central areas of the program is artificial intelligence and deep learning. Students learn how AI-based methods can be applied to medical data and healthcare problems. Deep learning can be particularly useful for recognizing complex patterns in large datasets and is increasingly used in areas such as medical image analysis. Another important area is medical image processing. Medical imaging plays a major role in modern diagnostics, for example through technologies that produce detailed images of organs, tissues, or other structures in the human body. Students learn how image-processing methods and artificial intelligence can be used to extract meaningful information from clinical images.

The program also focuses on signal processing. Physiological signals contain valuable information about a person's health and can be collected through various medical devices and sensors. Students learn how such signals can be analyzed and interpreted using computational methods.

Data analytics is another fundamental component. Students work with complex medical datasets and learn how to transform raw data into useful information. This includes understanding data, identifying relevant patterns, and developing analytical approaches that can support healthcare applications.

The curriculum also includes assistive technologies and augmented reality. These technologies can help people with disabilities or support medical professionals in different tasks. Augmented reality, for example, can provide additional digital information within a physical environment and may have applications in medical training, diagnostics, or therapy.

From Data to Intelligent Applications

A key objective of the program is to teach students how to transform complex data into practical applications. Instead of treating data analysis as an isolated technical activity, students learn how analytical results can become part of useful healthcare systems.

One example is clinical imaging. Medical images can be analyzed using advanced computational techniques to support diagnostic processes. AI-based systems may identify patterns or abnormalities that can provide additional information to healthcare professionals.

Another example is the analysis of physiological signals. Signals generated by the human body can be processed and analyzed to identify relevant characteristics or changes. Such applications demonstrate how engineering, mathematics, computer science, and medicine can work together.

AI can also be used to support clinical decision-making. In this context, technology does not necessarily replace medical professionals but can provide additional information that helps them make better-informed decisions. Understanding how such systems should be designed and evaluated is therefore an important part of modern medical engineering.

Practical Projects and Industry Collaboration

The program places a strong emphasis on practical application. Students work on real-world problems from healthcare and research and develop technological solutions using methods such as deep learning, image processing, signal processing, and data analytics.

Practical projects can address specific challenges in the healthcare sector, including AI-supported analysis of medical images, assistive systems, and augmented reality applications. Students have the opportunity to work with companies and research institutions and apply current technologies to realistic problems.

This approach allows students to gain experience that goes beyond theoretical knowledge. They learn how to define a problem, analyze requirements, select appropriate technologies, develop a solution, and evaluate the results. Working on projects also helps students develop professional skills such as teamwork, communication, problem-solving, and project management. These abilities are important because healthcare technology projects often involve interdisciplinary teams consisting of engineers, computer scientists, medical professionals, researchers, and business specialists.

The university's modern laboratories provide additional opportunities for hands-on learning and research. The facilities are equipped with specialized technologies and are designed to support projects in engineering, computer science, and health.

Flexible Learning with the HyFlex Model

One of the most distinctive features of the Medical Engineering & Analytics program is its HyFlex learning model. Students can participate in classes on campus or online, with some components also available asynchronously. This creates a high level of flexibility and makes the program particularly suitable for people who want to combine their studies with work or other responsibilities.

Teaching generally takes place from Monday to Thursday from 3:00 p.m., on Fridays from 1:30 p.m., and approximately two to three Saturdays per semester. Participation can be either on campus or online, depending on the course and format.

This structure is particularly attractive to working professionals. Students can remain employed while completing their Master’s degree and can potentially apply the knowledge they acquire directly in their professional environment.

The flexible structure also makes the program accessible to students who may not be able to attend every course physically. At the same time, the campus-based components provide opportunities for interaction, practical work, and collaboration with other students and lecturers.

Admission Requirements

The Master’s program is primarily aimed at graduates of technical or science-related Bachelor’s degree programs who are interested in medical technology, artificial intelligence, data analytics, and digital healthcare. An important advantage is that applicants do not necessarily need previous specialized knowledge of artificial intelligence or data analytics. While basic knowledge of technical or computer science subjects is helpful, the specific methods used in AI, deep learning, and data analytics are systematically taught during the Master’s program.

The university evaluates whether an applicant's previous education meets the admission requirements as part of the admission process. This means that graduates from different technical or scientific backgrounds may be able to enter the program depending on their academic qualifications.

Since the program is taught entirely in English, students are expected to have at least a B2 level of English. This is important for understanding technical literature, participating in lectures, working on projects, and communicating in an international academic and professional environment.

Entrepreneurship and Innovation

The program does not focus exclusively on technical development. Students are also encouraged to think about how technological ideas can become practical innovations.

Entrepreneurship modules teach students how to develop products, business models, and startup ideas in the fields of medical technology and digital healthcare. This can be particularly valuable for students who are interested in creating their own company or bringing innovative healthcare technologies to the market.

Developing a successful medical technology solution requires more than technical expertise. Engineers also need to understand the needs of users, the healthcare environment, possible business models, and the process of turning an idea into a viable product.

The combination of technical education and entrepreneurship therefore gives students the opportunity to approach healthcare innovation from both an engineering and a business perspective.

International Experience

International experience is another possible component of the program. Students have the opportunity to spend a semester abroad at selected partner universities. The university's International Relations Office supports students in planning and organizing their stay abroad.

An international experience can be particularly valuable in medical engineering because healthcare technologies are developed and used in international markets. Studying abroad can help students gain new perspectives, improve their intercultural skills, and build international academic and professional networks.

Career Opportunities

The growing digitalization of healthcare is creating demand for specialists who can combine technical expertise with knowledge of medical applications. Graduates of the Medical Engineering & Analytics program can therefore pursue careers in a wide range of areas.

Possible fields include medical technology, healthcare IT, digital health, artificial intelligence, data analytics, medical imaging, research, and healthcare innovation. Graduates can work for technology companies, medical device manufacturers, healthcare institutions, research organizations, or companies developing digital health solutions.

Possible professional roles include Medical Engineer, Data Analyst, AI Specialist, Machine Learning Engineer, Healthcare IT Specialist, Digital Health Specialist, or Research Engineer.

The interdisciplinary nature of the program is particularly valuable for these careers. Graduates understand both the technical methods used to process and analyze data and the healthcare context in which these technologies are applied.

The program can also prepare students for specialist and leadership positions in digital healthcare. Those interested in entrepreneurship may use their knowledge to develop their own products or establish a startup.

Study Structure and Costs

The Medical Engineering & Analytics Master’s program consists of 120 ECTS credits and takes four semesters. The qualification awarded is a Master of Science in Engineering. The program starts in the winter semester, with the semester beginning in October, and is offered at the CU campus in Klagenfurt.

The combination of a four-semester structure, English-language teaching, flexible HyFlex participation, practical projects, and industry cooperation makes the program suitable for both recent graduates and professionals.


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Our University of Applied Sciences

Since its founding, CU Carinthia University has evolved into a unique educational institution in Carinthia. Through the continuous development of its Degree programs and close collaboration with industry and academia, the university ensures that students receive a forward-looking education that is in step with the times.

CU Carinthia University embodies the principle of direct practical relevance. Full-time and part-time faculty members, as well as guest lecturers from industry and business, ensure an interdisciplinary, internationally oriented higher education experience. Currently, approximately 40 Degree programs in the fields of civil engineering, technology, health and social services, and business are offered at the campuses in Spittal, Villach, Klagenfurt and Feldkirchen.