Your Responsibilities
Write a PhD Thesis in the field of mathematics and scientific computing
Participate in projects conducted by the research group: BioTechMed-Graz Young Research Group "CARDIOPHYDAT"
Conduct research in the fields of machine learning, and scientific computing, with applications in cardiology, that will conduct to the completion of a PhD thesis
Writing scientific articles
Participation and presentation at international conferences
Development of own initiative and independent ideas
Student support and supervision
Collaboration on organisational and administrative tasks as well as evaluation measures
Your Profile
Completed diploma or master's degree in the field of applied mathematics, physics, biomedical engineering, software engineering
Knowledge of English to Level C1 TOEFL (at least 95 points) or IELTS (at least 7 points) or Cambridge Certificate in Advanced English (at least 180 points) or proof of at least 8 years English in school, or equivalent
Software skills in the fields: Programming (C++, python), Linux, High-Performance-Computing (MPI) advantageous
Demonstrated proficiency in at least one of the following: Machine Learning, Reduced Order Models, Finite Element Method, Medical Imaging, Numerical Mathematics
Highly committed and motivated approach to academic work
Independent, goal-oriented approach to work
Ability to work in international and interdisciplinary teams
We Offer
Meaning: We offer meaningful work for the world of tomorrow.
Our internal continuing education program is as colorful as the university itself.
Collaboration: With us, you'll find interdisciplinary, cross-professional opportunities to work together.
Benefits: Of course, there are all the usual benefits, from A "access to healthcare services" to Z "Zero emission goal".
Diversity: Besides our various scientific fields and their related issues, we offer a working environment in which diversity and inclusion are lived.
Flexibility: We demonstrate flexibility not only with the various working time models but also through the offers for the compatibility of family and career.
We offer an annual gross salary of at least € 52,865.40 for a fulltime position. An overpayment based on qualification and experience is possible.
Equality Principle
The University of Graz strives to increase the proportion of women in particular in management and faculty positions and therefore encourages qualified women to apply. In the event of underrepresentation, women with equal qualifications are generally given priority for admission. We welcome applications from persons with disabilities who meet the requirements of the advertised position.
Please note that in order to comply with the applicable data protection regulations, we can only accept applications via our web-based applicant tool for this vacant position.
Application Documents
The Following Documents Are Required For a Complete Application
motivation letter
recommendation letter
CV
PDF of master thesis
academic track records including grades
Proof of the language skills, required in the curriculum, if the doctoral program is not completed in the first language.
Organisational Unit
Department of Mathematics and Scientific Computing
The Young Research Group (YRG) ''CARDIOPHYDAT: CARDIOvascular PHYsics and DATa integration for digital twins" at the Department of Mathematics and Scientific Computing of the University of Graz -- funded by BioTechMed-Graz -- invites applications for one PhD student in the field of computational modeling of human cardiac function. The goal of this project is to develop novel core methodologies for the accurate subject-specific calibration of cardiac electromechanical (EM) models from clinical data. In particular, efficient and robust personalised cardiac digital twins based on physics-informed machine learning methodologies will be generated to estimate the local distribution of patient-specific biophysical and mechanical properties and infer crucial clinical biomarkers using in vivo clinical measurements.
In the context of this project, the PhD thesis will focus on: The extension of the inverse problem method [1,2] to biophysically accurate EM models of cardiovascular function, to simultaneously determine constant and spatially heterogeneous biophysical parameters, e.g., passive and active stress parameters.
The integration of real clinical data in the inverse problem methodology and the employment of the proposed methodology for the assessment of scars in the cardiac tissue using displacement data retrieved from Cine CMR sequences.
References
Caforio, F., Regazzoni, F., Pagani, S., Karabelas, E., Augustin, C., Haase, G., ... and Quarteroni, A Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models. Computational Mechanics, 75(2),
Höfler, M., Regazzoni, F., Pagani, S., Karabelas, E., Augustin, C., Haase, G., ... and Caforio, F Physics-informed neural network estimation of active material properties in timedependent cardiac biomechanical models. arXiv preprint arXiv:
About Us
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Contact
Dr. Federica Caforio | federica.-
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