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Medical Informatics, Biostatistics & Complex Systems

PhD Programm (UN094)
Programmkurse
Unterrichtssprache
Nächster Start
Kosten und Finanzierung
Programm Koordinator:in

Peter Klimek
Franz König
Administration: Susanne Rom, Michael Cibena

Studienaufbau

PhD Programmaufbau (UN094)
Dauer: 6 Semester (180 ECTS)
Abschluss: PhD

This PhD program aims at students with a mathematical, statistical, computational, or similar background who desire to advance biomedical sciences by applying their skills in technical, natural and formal sciences. We aim at forming a compact community of young natural scientists interacting with life scientists.

This PhD program covers statistical, mathematical, computational and related fields with direct relevance to biomedical research and beyond. Graduates will be able to independently conduct cutting-edge research that contributes to the rapid development of data-driven natural, technical, and formal approaches in the interdisciplinary life sciences.

The broad scope of this PhD program is both a challenge and a strength.

The projects offered to the PhD students will in most cases be closely related to one of the research foci of the Medical University of Vienna. Special emphasis is placed on topics that are addressed at the Center for Medical Data Science, including interdisciplinary applications of computer science, statistics, or complex systems research in the life sciences.
The basic lecture is designed to provide a broad understanding for all participants. Basic lectures from other PhD programs at the MUW, e.g. Biomedical Engineering, are accredited and can be chosen by students (with the approval of the coordinator) to work on specific topics not covered by the courses of this program. In addition, seminars - which may be held in conjunction with working group meetings - will allow in-depth study of specific research topics.

Finally, the broad scope of this program, which includes required courses in biostatistics, will provide a comprehensive methodological background and sufficient expertise in experimental design and data analysis.


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