Meet our first-class research and development team, working to develop advanced methods tailored to each of our speciality sectors.
Dr. Hannes Dörfler
Dr Hannes Dörfler is a chemist by training and received his PhD from the Molecular Systems Biology Department at the University of Vienna.
After a three-year postdoctoral phase at the company Boehringer Ingelheim in Germany where he was working on Omics-based biomarkers, he joined DPU as a staff scientist.
Hannes Dörfler has expertise in biochemistry and pharmaceutical development, and also works with multivariate statistical analysis of big data towards pattern recognition and biological interpretation.
Dr Sebastian Fitzek is a university lecturer at the Faculty of Communication and Public Relations (National School of Political and Administrative Studies) and a scientific researcher at the Institute for Quality of Life Research at the Romanian Academy. He specialises in qualitative research methods.
In December 2010 he obtained his PhD in sociology from the West University of Timişoara, and in October 2015 completed his postdoctoral studies in social sciences as part of the ‘Pluri- and interdisciplinarity in doctoral and postdoctoral programmes’ project run by the Romanian Academy.
His main areas of interest and research are communication sciences, social sciences and political sciences. Over the past 15 years, he has coordinated several series of projects and research work in the fields of political leadership, public image, political anthropology, sociology, social work and public health. Sebastian Fitzek has participated in over 50 conferences [MM1] and has authored and co-authored nationally and internationally scientifically indexed articles and chapters.
Dr. Sebastian Fitzek
Dr. Geevarghese George
Dr Geevarghese George obtained his PhD in physics from the University of Strasbourg (France) for his work at the Institut Charles Sadron (Theory and Simulation of Polymers group, CNRS), supervised by Dr Joachim Wittmer.
His thesis was on the study of statistical and rheological properties of freestanding polymer films using numerical modelling. During his PhD, he also developed an interest in machine learning and pivoted into the field through self-directed projects and coursework.
His current research interest is in the domain of deep learning and computer vision for medical diagnosis.
Dr Amirreza Mahbod obtained his BSc and first MSc degrees in electrical engineering from Iran University of Science and Technology in Tehran. He also received a second MSc in biomedical engineering from the KTH Royal Institute of Technology, Stockholm, Sweden.
Amirreza Mahbod completed his PhD in 2020 from the Medical University of Vienna, Austria, where he served as an industrial PhD fellow, working jointly at the Medical University of Vienna and TissueGnostics GmbH. For his PhD thesis, he mainly worked on the segmentation and classification of various structures and tissues in microscopic images.
Since 2020, he has been a postdoctoral fellow at the Institute of Pathophysiology and Allergy Research at the Medical University of Vienna. He will start his new role as an AI researcher at the Medical Image Analysis and Artificial Intelligence group at Danube Private University in August 2022.
Amirreza Mahbod's main areas of research are medical image analysis, computer vision, machine learning and deep learning, on which topics he has published several articles in peer-reviewed journals and conferences. He is particularly interested in developing novel deep learning-based methods for histological image analysis.
Dr. Amirreza Mahbod
Camilla Neubauer obtained her BSc degree in midwifery from the Health University of Applied Sciences Tyrol and holds an MA in Management of Health Institutions from the IMC University of Applied Sciences Krems. She is currently studying for a doctorate in Management and Economics in Healthcare at the UMIT TIROL, the Private University for Health Sciences and Health Technology in Hall in Tyrol.
She works as an academic associate for Cochrane Austria in the Department for Evidence-Based Medicine and Evaluation at the University for Continuing Education Krems. Her responsibilities there include conducting team-based rapid reviews for the evidence-based information centre for nurses.
In her thesis she investigated strategies for ensuring the provision of obstetrics care in a specific rural area of Austria, by triangulating qualitative methods employed within a context of cross-border cooperation. In her dissertation, she endeavours to identify options for providing safe, economic and resource-efficient pre- and postnatal care on an ongoing basis, especially in rural settings.
While she has experience in generating evidence synthesis, her research interests also include rural healthcare, cross-border healthcare, staff and organisational development, and healthcare workforce research and analysis.
BSc MA (Researcher)
Dr. Florian Schwarzhans
Florian Schwarzhans holds an MSc degree in Medical Informatics from the Medical University of Vienna.
He has a background in informatics with a focus on programming using C, C++, MatLab and Python, as well as a background in electronics specializing in biomedical engineering.
His research interests include medical image processing with a special focus on automatic graph-based segmentation algorithms, deep learning methods for both image classification and segmentation, and the development and implementation of parallel algorithms for medical image processing and analysis using CUDA.
He has developed real-time retinal tracking software for a prototype PS-OCT system and software for fast parallel reconstruction of OCT volumes from raw data via the GPU, which is actively being used in multiple OCT systems.
Dr.in Olgica Zaric
Dr. Olgica Zaric did her PhD studies at the Medical University of Vienna, where she worked as a researcher on the development and implementation of multi-parametric magnetic resonance imaging (MRI) techniques. Together with conventional T1, T2, and contrast-enhanced MRI, this included diffusion-weighted imaging (DWI), sodium imaging (23Na-MRI) and chemical exchange saturation transfer (CEST) MRI at ultra-high fields. Her major focus was investigating the feasibility of these imaging modalities and their translation into clinical practice.
The research was based on establishing biochemical imaging modalities and corresponding quantitative imaging markers, which are reliable and applicable for breast lesion diagnosis and characterization as well as treatment monitoring. She has worked on protocol optimization, fast image acquisition methods, evaluation and verification of image post-processing methods, quantitative imaging data analysis, etc.
In the future, she plans to extend her expertise in the area of image analysis, processing and deep learning, and branch out to other methods that may be potentially applicable and transferrable to different diagnostic and therapy approaches.