Simone Schanitz
Simone Schanitz was already very interested in science and mathematics when she was at school. That's why it wasn't difficult for her to choose a…
Medical engineering is a recently emerged field that applies engineering, physics, mathematics, and computer science to health care in order to improve diagnostic tools, monitoring techniques, and therapeutic devices. The field of medical engineering is heavily involved in research and development and covers topics from tissue engineering and robotics, to development of medical devices and wearable sensors. The analytics field aims to find patterns in data to allow the automatization of the data processing and, thus, diagnostic assessment. These developments play a role as a major game-changer in our healthcare system and thus in our society.
The master of science program "Medical Engineering and Analytics" is designed to educate a new generation of medical engineers, who will innovate the healthcare systems of the future. The MSc program puts the main emphasis on the current trends in medical engineering –the use of artificial intelligence (AI), augmented and virtual reality (VR, AR), and as a result of the demographic change, assistive technologies.
AI already supports the evaluation of medical data and images nowadays, and in the future, it may also affect many other areas that we are not yet aware of. AR is being used for the teaching of medical doctors or for surgery planning but still requires more profound development to reach its full potential. The aging of the population is another important point shaping the future of healthcare systems worldwide and leads to vast developments and increased use of assistive technologies. With the main focus on these current trends, the MSc degree program in Medical Engineering and Analytics encompasses several courses with the topics deep learning, signal and image analysis, visualization, and assisted living. We also focus on entrepreneurship and provide the students with resources and assistance to found their own startups.
Additionally to theoretical input, hands-on training in specialized laboratories and work on projects represent other core elements of this MSc program. Due to close cooperation with the healthcare sector, healthcare professionals and experts can be invited as guest lecturers. Small groups, personal support and mentoring, and an open-door policy for all professors are our top priorities. Since internationalization is an important aspect of modern education, the students are encouraged and supported to go for an internship abroad in joint projects with our research partners.
Graduates of the Medical Engineering and Analytics master of science program have a wide spectrum of exciting career opportunities, often finding employment in medical device production companies, software development companies, hospitals, cutting-edge start-ups or as entrepreneurs themselves.
Are you interested in studying “Medical Engineering and Analytics” online while maintaining the highest standards? Our HyFlex MSc program in “Medical Engineering and Analytics” allows you to study online with the possibility to do some lectures asynchronously at your own schedule and pace. Therefore, you can study from anywhere in the world and also combine the studies with your work and family life. We ensure the highest European educational standards at low tuition fees.
Are you interested in studying Medical Engineering and Analytics on-site but want to work part-time as well? The MSc program in “Medical Engineering and Analytics” is designed in a work-friendly way with the lectures given in the afternoon and on 2-3 Saturdays per semester.
This program offers our students the chance to work part-time at one of our partner companies. With partner companies such as KABEG (Carinthia Hospital Management Company), PCS Professional Clinical Software and SOT Medical Systems, this program allows the students to gain professional experience in Carinthia's leading companies already during their studies.
This Master's program is organized in a work-friendly way and is therefore compatible with a professional career –> study onsite, online and/or even asynchronously
With over 100 Study & Work partner companies and organizations, CUAS offers students the opportunity to combine studying and working!
The Master of Science degree study program"Medical Engineering and Analytics" enables graduates to face the challenges of the modern healthcare sector. Equipped with knowledge of artificial intelligence, extended reality, and assistive technologies, graduates are sought-after by a wide variety of potential employers inside the health care industry as well outside of it.
Thanks to the Study & Work program, students can gain professional experience at Carinthia's leading companies already during their studies, ensuring a position for the vast majority of our graduates. Alternatively, the graduates of the MSc degree program "Medical Engineering and Analytics" can use their knowledge to pursue further scientific specialization in the form of a PhD or, due to the focus on entrepreneurship and assistance provided by CUAS, to found their own startups.
A Bachelor's degree in a relevant field such as medical technology, biomedical engineering, medical informatics or data science is required to be eligible. Additionally, potential students should demonstrate strengths in the following areas:
Lecture | Type | SPPS | ECTS-Credits | Course number |
---|---|---|---|---|
Active Assisted Living 2 | ILV | 3,0 | 5,0 | M2.03100.20.010 |
Applied Medical Image Analysis | ILV | 3,0 | 5,0 | M2.03100.20.030 |
Augmented Visualization in Medicine | ILV | 3,0 | 5,0 | M2.03100.20.040 |
Data Analytics and Neural Networks | ILV | 3,0 | 5,0 | M2.03100.20.020 |
Project (II) - Frameworks and Concept Study | PA | 0,3 | 5,0 | M2.03100.20.060 |
Smart Medical Production and Robotics | ILV | 3,0 | 5,0 | M2.03100.20.050 |
Lecture | Type | SPPS | ECTS-Credits | Course number |
---|---|---|---|---|
Master Exam | ME | 0,0 | 3,0 | M2.03100.40.030 |
Master Thesis | MT | 0,5 | 25,0 | M2.03100.40.010 |
Master Thesis Seminar | SE | 2,0 | 2,0 | M2.03100.40.020 |
Lecture | Type | SPPS | ECTS-Credits | Course number |
---|---|---|---|---|
Active Assisted Living 2 | ILV | 3,0 | 5,0 | M2.03100.20.010 |
Applied Medical Image Analysis | ILV | 3,0 | 5,0 | M2.03100.20.030 |
Augmented Visualization in Medicine | ILV | 3,0 | 5,0 | M2.03100.20.040 |
Data Analytics and Neural Networks | ILV | 3,0 | 5,0 | M2.03100.20.020 |
Project (II) - Frameworks and Concept Study | PA | 0,3 | 5,0 | M2.03100.20.060 |
Smart Medical Production and Robotics | ILV | 3,0 | 5,0 | M2.03100.20.050 |
Lecture | Type | SPPS | ECTS-Credits | Course number |
---|---|---|---|---|
Master Exam | ME | 0,0 | 3,0 | M2.03100.40.030 |
Master Thesis | MT | 0,5 | 25,0 | M2.03100.40.010 |
Master Thesis Seminar | SE | 2,0 | 2,0 | M2.03100.40.020 |
Lecture | Type | SPPS | ECTS-Credits | Course number |
---|---|---|---|---|
Academic Skills | SE | 3,0 | 5,0 | M2.08860.10.061 |
Active Assisted Living 1 | ILV | 3,0 | 5,0 | M2.03100.10.010 |
Applied Medical Signal Analysis | ILV | 3,0 | 5,0 | M2.03100.10.030 |
Introduction to Machine Learning | ILV | 3,5 | 5,0 | M2.08760.11.051 |
Project (I) - Prerequisites and Domains | PA | 3,0 | 5,0 | M2.03100.10.060 |
Statistics | ILV | 3,5 | 5,0 | M2.08760.11.021 |
Lecture | Type | SPPS | ECTS-Credits | Course number |
---|---|---|---|---|
Artificial Intelligence in Clinical Imaging | ILV | 3,0 | 5,0 | M2.03100.30.030 |
Deep Learning in Medical Applications | ILV | 3,0 | 5,0 | M2.03100.30.020 |
Entrepreneurship | SE | 3,0 | 5,0 | M2.03100.30.040 |
Neuroscience | ILV | 3,0 | 5,0 | M2.03100.30.050 |
Project (III) - Pracical Implementation | PA | 0,3 | 5,0 | M2.03100.30.060 |
Studies in Biomedical Engineering | ILV | 3,0 | 5,0 | M2.03100.30.010 |
Lecture | Type | SPPS | ECTS-Credits | Course number |
---|---|---|---|---|
Active Assisted Living 2 | ILV | 3,0 | 5,0 | M2.03100.20.010 |
Active Assisted Living 2 | ILV | 3,0 | 5,0 | M2.03100.20.010 |
Applied Medical Image Analysis | ILV | 3,0 | 5,0 | M2.03100.20.030 |
Applied Medical Image Analysis | ILV | 3,0 | 5,0 | M2.03100.20.030 |
Artificial Neural Networks and Deep Learning (I) | ILV | 3,5 | 5,0 | M2.08760.11.131 |
Augmented Visualization in Medicine | ILV | 3,0 | 5,0 | M2.03100.20.040 |
Augmented Visualization in Medicine | ILV | 3,0 | 5,0 | M2.03100.20.040 |
Data Analytics and Neural Networks | ILV | 3,0 | 5,0 | M2.03100.20.020 |
Project (II) - Frameworks and Concept Study | PA | 3,0 | 5,0 | M2.03100.20.060 |
Project (II) - Frameworks and Concept Study | PA | 0,3 | 5,0 | M2.03100.20.060 |
Smart Medical Production and Robotics | ILV | 3,0 | 5,0 | M2.03100.20.050 |
Smart Medical Production and Robotics | ILV | 3,0 | 5,0 | M2.03100.20.050 |
Lecture | Type | SPPS | ECTS-Credits | Course number |
---|---|---|---|---|
Master Exam | ME | 0,0 | 3,0 | M2.03100.40.030 |
Master Exam | ME | 0,0 | 3,0 | M2.03100.40.030 |
Master Thesis | MT | 0,5 | 25,0 | M2.03100.40.010 |
Master Thesis | MT | 0,5 | 25,0 | M2.03100.40.010 |
Master Thesis Seminar | SE | 2,0 | 2,0 | M2.03100.40.020 |
Master Thesis Seminar | SE | 2,0 | 2,0 | M2.03100.40.020 |
Lecture | Type | SPPS | ECTS-Credits | Course number |
---|---|---|---|---|
Active Assisted Living 2 | ILV | 3,0 | 5,0 | M2.03100.20.010 |
Active Assisted Living 2 | ILV | 3,0 | 5,0 | M2.03100.20.010 |
Applied Medical Image Analysis | ILV | 3,0 | 5,0 | M2.03100.20.030 |
Applied Medical Image Analysis | ILV | 3,0 | 5,0 | M2.03100.20.030 |
Artificial Neural Networks and Deep Learning (I) | ILV | 3,5 | 5,0 | M2.08760.11.131 |
Augmented Visualization in Medicine | ILV | 3,0 | 5,0 | M2.03100.20.040 |
Augmented Visualization in Medicine | ILV | 3,0 | 5,0 | M2.03100.20.040 |
Data Analytics and Neural Networks | ILV | 3,0 | 5,0 | M2.03100.20.020 |
Project (II) - Frameworks and Concept Study | PA | 3,0 | 5,0 | M2.03100.20.060 |
Project (II) - Frameworks and Concept Study | PA | 0,3 | 5,0 | M2.03100.20.060 |
Smart Medical Production and Robotics | ILV | 3,0 | 5,0 | M2.03100.20.050 |
Smart Medical Production and Robotics | ILV | 3,0 | 5,0 | M2.03100.20.050 |
Lecture | Type | SPPS | ECTS-Credits | Course number |
---|---|---|---|---|
Master Exam | ME | 0,0 | 3,0 | M2.03100.40.030 |
Master Exam | ME | 0,0 | 3,0 | M2.03100.40.030 |
Master Thesis | MT | 0,5 | 25,0 | M2.03100.40.010 |
Master Thesis | MT | 0,5 | 25,0 | M2.03100.40.010 |
Master Thesis Seminar | SE | 2,0 | 2,0 | M2.03100.40.020 |
Master Thesis Seminar | SE | 2,0 | 2,0 | M2.03100.40.020 |
Lecture | Type | SPPS | ECTS-Credits | Course number |
---|---|---|---|---|
Academic Skills | SE | 3,0 | 5,0 | M2.08860.10.061 |
Academic Skills | SE | 3,0 | 5,0 | M2.08860.10.061 |
Active Assisted Living 1 | ILV | 3,0 | 5,0 | M2.03100.10.010 |
Active Assisted Living 1 | ILV | 3,0 | 5,0 | M2.03100.10.010 |
Applied Medical Signal Analysis | ILV | 3,0 | 5,0 | M2.03100.10.030 |
Applied Medical Signal Analysis | ILV | 3,0 | 5,0 | M2.03100.10.030 |
Introduction to Machine Learning | ILV | 3,5 | 5,0 | M2.08760.11.051 |
Introduction to Machine Learning | ILV | 3,5 | 5,0 | M2.08760.11.051 |
Project (I) - Prerequisites and Domains | PA | 3,0 | 5,0 | M2.03100.10.060 |
Project (I) - Prerequisites and Project Domains | PA | 3,0 | 5,0 | M2.03100.10.060 |
Statistics | ILV | 3,5 | 5,0 | M2.08760.11.021 |
Statistics | ILV | 3,5 | 5,0 | M2.08760.11.021 |
Lecture | Type | SPPS | ECTS-Credits | Course number |
---|---|---|---|---|
Artificial Intelligence in Clinical Imaging | ILV | 3,0 | 5,0 | M2.03100.30.030 |
Artificial Intelligence in Clinical Imaging | ILV | 3,0 | 5,0 | M2.03100.30.030 |
Artificial Neural Networks and Deep Learning (II) | ILV | 3,5 | 5,0 | M2.08760.11.141 |
Deep Learning in Medical Applications | ILV | 3,0 | 5,0 | M2.03100.30.020 |
Entrepreneurship | SE | 3,0 | 5,0 | M2.03100.30.040 |
Entrepreneurship | SE | 3,0 | 5,0 | M2.03100.30.040 |
Neuroscience | ILV | 3,0 | 5,0 | M2.03100.30.050 |
Neuroscience | ILV | 3,0 | 5,0 | M2.03100.30.050 |
Project (III) - Pracical Implementation | PA | 0,3 | 5,0 | M2.03100.30.060 |
Project (III) - Practical Implementation | PA | 3,0 | 5,0 | M2.03100.30.060 |
Studies in Biomedical Engineering | ILV | 3,0 | 5,0 | M2.03100.30.010 |
Studies in Biomedical Engineering | ILV | 3,0 | 5,0 | M2.03100.30.010 |
Lecture | Type | SPPS | ECTS-Credits | Course number |
---|---|---|---|---|
Academic Skills | SE | 3,0 | 5,0 | M2.08860.10.061 |
Academic Skills | SE | 3,0 | 5,0 | M2.08860.10.061 |
Active Assisted Living 1 | ILV | 3,0 | 5,0 | M2.03100.10.010 |
Active Assisted Living 1 | ILV | 3,0 | 5,0 | M2.03100.10.010 |
Applied Medical Signal Analysis | ILV | 3,0 | 5,0 | M2.03100.10.030 |
Applied Medical Signal Analysis | ILV | 3,0 | 5,0 | M2.03100.10.030 |
Introduction to Machine Learning | ILV | 3,5 | 5,0 | M2.08760.11.051 |
Introduction to Machine Learning | ILV | 3,5 | 5,0 | M2.08760.11.051 |
Project (I) - Prerequisites and Domains | PA | 3,0 | 5,0 | M2.03100.10.060 |
Project (I) - Prerequisites and Project Domains | PA | 3,0 | 5,0 | M2.03100.10.060 |
Statistics | ILV | 3,5 | 5,0 | M2.08760.11.021 |
Statistics | ILV | 3,5 | 5,0 | M2.08760.11.021 |
Lecture | Type | SPPS | ECTS-Credits | Course number |
---|---|---|---|---|
Artificial Intelligence in Clinical Imaging | ILV | 3,0 | 5,0 | M2.03100.30.030 |
Artificial Intelligence in Clinical Imaging | ILV | 3,0 | 5,0 | M2.03100.30.030 |
Artificial Neural Networks and Deep Learning (II) | ILV | 3,5 | 5,0 | M2.08760.11.141 |
Deep Learning in Medical Applications | ILV | 3,0 | 5,0 | M2.03100.30.020 |
Entrepreneurship | SE | 3,0 | 5,0 | M2.03100.30.040 |
Entrepreneurship | SE | 3,0 | 5,0 | M2.03100.30.040 |
Neuroscience | ILV | 3,0 | 5,0 | M2.03100.30.050 |
Neuroscience | ILV | 3,0 | 5,0 | M2.03100.30.050 |
Project (III) - Pracical Implementation | PA | 0,3 | 5,0 | M2.03100.30.060 |
Project (III) - Practical Implementation | PA | 3,0 | 5,0 | M2.03100.30.060 |
Studies in Biomedical Engineering | ILV | 3,0 | 5,0 | M2.03100.30.010 |
Studies in Biomedical Engineering | ILV | 3,0 | 5,0 | M2.03100.30.010 |
Research and development make an important contribution to transforming broad university knowledge into practical application solutions and to promoting cooperation between companies and universities.
The Master of Science “Medical Engineering and Analytics” degree program is designed to educate and train highly qualified specialists in the areas of new health technologies and data intelligence.
The successful completion of the master of science program qualifies graduates to undertake a doctorate.
FH-Prof. Priv.-Doz. Dipl.-Ing. (FH)
Günther Grabner, Ph.D.Mag.a
Michaela FilippitschFH-Prof. DI Dr.
Johannes OberzaucherFH-Prof. DI (FH) Dr. techn.
Markus ProsseggerFH-Prof.in Dipl.-Ing. Dr. techn.
Daniela Elisabeth Ströckl, BScFH-Prof.in Priv.-Doz. DI Mag.a Dr.in rer. nat.
Anita Kloss-BrandstätterFH-Prof. DI Dr.
Josef TuppingerFH-Prof. Dr. habil.
Pascal NicolayDI Dr. techn.
Andreas Daniel HartlDipl.-Ing. Dr. techn.
Mathias BrandstötterDipl.-Ing. (FH) Mag. (FH)
Michael RothDI
Beáta Bachratá, Ph.D.Mag. Dr.
Florian FischmeisterDI
Stanislav MotykaDI Dr.in techn.
Olivia PfeilerMag. DI Dr. techn.
Kathrin PlankensteinerPrimoschgasse 8-10
9020 Klagenfurt am Wörthersee, Austria
+43 5 90500 3301
klagenfurt[at]fh-kaernten[dot]at
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